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Oregon State University
1.
Anderson, Jessica.
What says your heart: community, learning, and education in a Nahua indigenous village.
Degree: MA, Applied Anthropology, 2016, Oregon State University
URL: http://hdl.handle.net/1957/59993 
► The purpose of this thesis is to understand the experiences that Nahua children, in rural Mexico, have as they attend schools that are primarily influenced…
(more)
▼ The purpose of this thesis is to understand the experiences that Nahua children, in rural Mexico, have as they attend schools that are primarily influenced by formal Western education in relation to their own ways of
learning and knowing. This research took place over the course of three months within a Nahua indigenous community. I completed fieldwork in both the local elementary school and within the community through participant observation and semi-structured interviews with children, parents, and teachers. What I found was that the ways in which the community constructed childhood, knowledge, and
learning differed significantly in what they experienced within the classroom. This in turn effected the relationships that were built between the school and the community and the student’s ability to succeed and have meaningful and supportive educational experiences. The experiences that they have are in turn effected by global, national, and local political and economic trends such as the implementation of neoliberalism, and it subsequent effect on educational policies for rural communities in Mexico. This ultimately leads to an education that is colonial in nature, despite efforts of including bilingual intercultural education programs, as the system seeks to colonize the bodies and minds of these students. Thus, it is my conclusion that both community and school must work together in order to create education programs that decolonize this process through the inclusion of programs that re-center their local language, culture, and
learning practices.
Advisors/Committee Members: Carpena-Mendez, Fina (advisor), Price, Lisa (committee member).
Subjects/Keywords: learning
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APA (6th Edition):
Anderson, J. (2016). What says your heart: community, learning, and education in a Nahua indigenous village. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/59993
Chicago Manual of Style (16th Edition):
Anderson, Jessica. “What says your heart: community, learning, and education in a Nahua indigenous village.” 2016. Masters Thesis, Oregon State University. Accessed March 01, 2021.
http://hdl.handle.net/1957/59993.
MLA Handbook (7th Edition):
Anderson, Jessica. “What says your heart: community, learning, and education in a Nahua indigenous village.” 2016. Web. 01 Mar 2021.
Vancouver:
Anderson J. What says your heart: community, learning, and education in a Nahua indigenous village. [Internet] [Masters thesis]. Oregon State University; 2016. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1957/59993.
Council of Science Editors:
Anderson J. What says your heart: community, learning, and education in a Nahua indigenous village. [Masters Thesis]. Oregon State University; 2016. Available from: http://hdl.handle.net/1957/59993
University of Rochester
2.
Tracey, Edward A. (1976 - ).
Firefighter workplace learning : an exploratory case
study.
Degree: EdD, 2014, University of Rochester
URL: http://hdl.handle.net/1802/28350
► Despite there being a significant amount of research investigating workplace learning, research exploring firefighter workplace learning is almost nonexistent. The purpose of this qualitative multi-case…
(more)
▼ Despite there being a significant amount of
research investigating workplace learning, research exploring
firefighter workplace learning is almost nonexistent. The purpose
of this qualitative multi-case study was to explore how
firefighters conceptualize, report, and practice workplace
learning. The researcher also investigated how firefighters learn
informally in the workplace and how that informal learning was
manifested. A qualitative multi-case research study of six
full-time career firefighters employed by a fire department in New
York State was conducted. Data were collected through field
observations, interviews, and document analysis. The data were
analyzed using grounded theory analysis as detailed by Charmaz
(2006). Several themes emerged from the data analysis revealing how
firefighters learn in the workplace. Findings indicate that
firefighters learn necessary workplace information through both
formal and informal learning practices. Firefighters learn formally
in the workplace by (a) attending the fire academy, (b)
participating in the in-service training programs, (c) taking
external fire service courses, (d) attending college-level fire
science programs, and (e) teaching and instructing. Firefighters
learn informally in the workplace (a) through practice, (b) from
each other, (c), through self-directed learning activities, and (d)
from prior exposure to the fire service. These findings highlighted
a complex, hybrid interaction between formal and informal workplace
learning activities. The findings present implications for both
fire service practice and policy. Findings from this study suggest
workplace learning may be enhanced through training fire officers
to identify and foster firefighter’s informal workplace learning
practices. The policy implications for fire department managers and
trainers include improving firefighter informal learning in the
fire service through the provision of support, resources, and time
for learning activities as well as by developing mechanisms to
record and document the time spent on informal learning
activities.
Subjects/Keywords: Workplace learning; Firefighter learning; Informal learning; Informal workplace learning; Adult learning
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APA (6th Edition):
Tracey, E. A. (. -. ). (2014). Firefighter workplace learning : an exploratory case
study. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/28350
Chicago Manual of Style (16th Edition):
Tracey, Edward A (1976 - ). “Firefighter workplace learning : an exploratory case
study.” 2014. Doctoral Dissertation, University of Rochester. Accessed March 01, 2021.
http://hdl.handle.net/1802/28350.
MLA Handbook (7th Edition):
Tracey, Edward A (1976 - ). “Firefighter workplace learning : an exploratory case
study.” 2014. Web. 01 Mar 2021.
Vancouver:
Tracey EA(-). Firefighter workplace learning : an exploratory case
study. [Internet] [Doctoral dissertation]. University of Rochester; 2014. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1802/28350.
Council of Science Editors:
Tracey EA(-). Firefighter workplace learning : an exploratory case
study. [Doctoral Dissertation]. University of Rochester; 2014. Available from: http://hdl.handle.net/1802/28350
University of Edinburgh
3.
Pang, Kunkun.
Learning about the learning process : from active querying to fine-tuning.
Degree: PhD, 2019, University of Edinburgh
URL: http://hdl.handle.net/1842/36031
► The majority of research on academic machine learning addresses the core model fitting part of the machine learning workflow. However, prior to model fitting, data…
(more)
▼ The majority of research on academic machine learning addresses the core model fitting part of the machine learning workflow. However, prior to model fitting, data collection and annotation is an important step; and subsequently to this, knowledge transfer to different but related problems is also important. Recently, the core model fitting step in this workflow has been upgraded using learning-to-learn methodologies, where learning algorithms are applied to improve the fitting algorithm itself in terms of computation or data efficiency. However, algorithms for data collection and knowledge transfer are still commonly hand-engineered. In this doctoral thesis, we upgrade the pre-and post-processing steps of the machine learning pipeline with the learningto- learn paradigm. We first present novel learning-to-learn approaches that improve the algorithms for this pre-processing step in terms of label efficiency. The inefficiency of data annotation is a common issue in the field: To fit the desired model, a large amount of data is usually collected and annotated, much of which is useless. Active learning aims to address this by selecting the most suitable data for annotation. Since conventional active learning algorithms are hand-engineered and heuristically designed for a specific problem, they typically cannot be adapted across nor even within datasets. The data efficiency of active learning can be improved either by online learning active learning within a specific problem, or by transferring active learning knowledge between related problems. We begin by investigating the framework of leaning active learning online, which learns to select the best criteria for a particular dataset as queries are made. It enables online adaptation, along with the state of the model and dataset changes, while guaranteeing performance. Subsequently, we upgrade the previous framework to a data-driven learning-based approach by learning a transferable active-learning policy end-to-end. The framework is thus capable of directly optimising the accuracy of the underlying classifier, and can adapt to the statistics of any given dataset. More importantly, the learned active-learning policy is domain agnostic and generalises to new learning problems. We next turn to knowledge transfer from a well-learned problem to a novel target problem. We develop a new learning-to-learn technique to improve the effectiveness and efficiency of fine-tuning-based transfer learning. Conventional transfer learning approaches are heuristic: Most commonly, small learning-rate stochastic gradient descent starting from the source model as a condition, and keeping the architecture constant. However, the typical transfer learning pipeline transfers learning from a general model or dataset to a more specific one. Thus, we propose a transfer learning algorithm for neural networks, which simultaneously prune the size of the target networks architecture and updates its weights. This enables the model complexity to be reduced, as training iterations increase, and both…
Subjects/Keywords: meta learning; active learning; transfer learning; reinforcement learning; bandit learning
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APA (6th Edition):
Pang, K. (2019). Learning about the learning process : from active querying to fine-tuning. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/36031
Chicago Manual of Style (16th Edition):
Pang, Kunkun. “Learning about the learning process : from active querying to fine-tuning.” 2019. Doctoral Dissertation, University of Edinburgh. Accessed March 01, 2021.
http://hdl.handle.net/1842/36031.
MLA Handbook (7th Edition):
Pang, Kunkun. “Learning about the learning process : from active querying to fine-tuning.” 2019. Web. 01 Mar 2021.
Vancouver:
Pang K. Learning about the learning process : from active querying to fine-tuning. [Internet] [Doctoral dissertation]. University of Edinburgh; 2019. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1842/36031.
Council of Science Editors:
Pang K. Learning about the learning process : from active querying to fine-tuning. [Doctoral Dissertation]. University of Edinburgh; 2019. Available from: http://hdl.handle.net/1842/36031
Cornell University
4.
Karampatziakis, Nikolaos.
Online Learning Algorithms For Sequence Prediction, Importance Weighted Classification, And Active Learning.
Degree: PhD, Computer Science, 2012, Cornell University
URL: http://hdl.handle.net/1813/31116
► This thesis studies three problems in online learning. For all the problems the proposed solutions are simple yet non-trivial adaptations of existing online machine learning…
(more)
▼ This thesis studies three problems in online
learning. For all the problems the proposed solutions are simple yet non-trivial adaptations of existing online machine
learning algorithms. For the task of sequential prediction, a modified multiplicative update algorithm that produces small and accurate models is proposed. This algorithm makes no assumption about the complexity of the source that produces the given sequence. For the task of online
learning when examples have varying importances, the proposed algorithm is a version of gradient descent in continuous time. Finally, for the task of efficient online active
learning, the implementation we provide makes use of many shortcuts. These include replacing a batch
learning algorithm with an online one, as well as a creative use of the aforementioned continuous time gradient descent to compute the desirability of asking for the label of a given example. As this thesis shows, online machine
learning algorithms can be easily adapted to many new problems.
Advisors/Committee Members: Kozen, Dexter Campbell (chair), Hooker, Giles J. (committee member), Joachims, Thorsten (committee member), Kleinberg, Robert David (committee member).
Subjects/Keywords: machine learning; online learning; active learning
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APA (6th Edition):
Karampatziakis, N. (2012). Online Learning Algorithms For Sequence Prediction, Importance Weighted Classification, And Active Learning. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/31116
Chicago Manual of Style (16th Edition):
Karampatziakis, Nikolaos. “Online Learning Algorithms For Sequence Prediction, Importance Weighted Classification, And Active Learning.” 2012. Doctoral Dissertation, Cornell University. Accessed March 01, 2021.
http://hdl.handle.net/1813/31116.
MLA Handbook (7th Edition):
Karampatziakis, Nikolaos. “Online Learning Algorithms For Sequence Prediction, Importance Weighted Classification, And Active Learning.” 2012. Web. 01 Mar 2021.
Vancouver:
Karampatziakis N. Online Learning Algorithms For Sequence Prediction, Importance Weighted Classification, And Active Learning. [Internet] [Doctoral dissertation]. Cornell University; 2012. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1813/31116.
Council of Science Editors:
Karampatziakis N. Online Learning Algorithms For Sequence Prediction, Importance Weighted Classification, And Active Learning. [Doctoral Dissertation]. Cornell University; 2012. Available from: http://hdl.handle.net/1813/31116
University of Waterloo
5.
Gaurav, Ashish.
Safety-Oriented Stability Biases for Continual Learning.
Degree: 2020, University of Waterloo
URL: http://hdl.handle.net/10012/15579
► Continual learning is often confounded by “catastrophic forgetting” that prevents neural networks from learning tasks sequentially. In the case of real world classification systems that…
(more)
▼ Continual learning is often confounded by “catastrophic forgetting” that prevents neural networks from learning tasks sequentially. In the case of real world classification systems that are safety-validated prior to deployment, it is essential to ensure that validated knowledge is retained. We propose methods that build on existing unconstrained continual learning solutions, which increase the model variance or weaken the model bias to better retain more of the existing knowledge.
We investigate multiple such strategies, both for continual classification as well as continual reinforcement learning. Finally, we demonstrate the improved performance of our methods against popular continual learning approaches, using variants of standard image classification datasets, as well as assess the effect of weaker biases in continual reinforcement learning.
Subjects/Keywords: deep learning; continual learning; classification; reinforcement learning
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APA (6th Edition):
Gaurav, A. (2020). Safety-Oriented Stability Biases for Continual Learning. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15579
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Gaurav, Ashish. “Safety-Oriented Stability Biases for Continual Learning.” 2020. Thesis, University of Waterloo. Accessed March 01, 2021.
http://hdl.handle.net/10012/15579.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Gaurav, Ashish. “Safety-Oriented Stability Biases for Continual Learning.” 2020. Web. 01 Mar 2021.
Vancouver:
Gaurav A. Safety-Oriented Stability Biases for Continual Learning. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/10012/15579.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Gaurav A. Safety-Oriented Stability Biases for Continual Learning. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/15579
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Guelph
6.
Im, Jiwoong.
Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems.
Degree: Master of Applied Science, School of Engineering, 2015, University of Guelph
URL: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809
► The objective of this thesis is to take the dynamical systems approach to understand the unsupervised learning models and learning algorithms. Gated auto-encoders (GAEs) are…
(more)
▼ The objective of this thesis is to take the dynamical systems approach to understand the unsupervised
learning models and
learning algorithms. Gated auto-encoders (GAEs) are an interesting and flexible extension of auto-encoders which can learn transformations among different images or pixel covariances within images. We examine the GAEs' ability to represent different functions or data distributions. We apply a dynamical systems view to GAEs, deriving a scoring function, and drawing connections to RBMs. In the second part of our study, we investigate the performance of Minimum Probability Flow (MPF)
learning for training restricted Boltzmann machines (RBMs). MPF proposes a tractable, consistent, objective function defined in terms of a Taylor expansion of the KL divergence with respect to sampling dynamics. We propose a more general form for the sampling dynamics in MPF, and explore the consequences of different choices for these dynamics for training RBMs.
Advisors/Committee Members: Taylor, Graham W (advisor).
Subjects/Keywords: Machine learning; Deep Learning; unsupervised learning
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APA ·
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APA (6th Edition):
Im, J. (2015). Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems. (Masters Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809
Chicago Manual of Style (16th Edition):
Im, Jiwoong. “Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems.” 2015. Masters Thesis, University of Guelph. Accessed March 01, 2021.
https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809.
MLA Handbook (7th Edition):
Im, Jiwoong. “Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems.” 2015. Web. 01 Mar 2021.
Vancouver:
Im J. Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems. [Internet] [Masters thesis]. University of Guelph; 2015. [cited 2021 Mar 01].
Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809.
Council of Science Editors:
Im J. Analyzing Unsupervised Representation Learning Models Under the View of Dynamical Systems. [Masters Thesis]. University of Guelph; 2015. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8809
Hong Kong University of Science and Technology
7.
Wellmer, Zachary William CSE.
Building and leveraging implicit models for policy gradient methods.
Degree: 2019, Hong Kong University of Science and Technology
URL: http://repository.ust.hk/ir/Record/1783.1-102375
;
https://doi.org/10.14711/thesis-991012757568703412
;
http://repository.ust.hk/ir/bitstream/1783.1-102375/1/th_redirect.html
► In this thesis, we study Policy Prediction Network and Policy Tree Network, both are deep reinforcement learning architectures offering ways to improve sample complexity and…
(more)
▼ In this thesis, we study Policy Prediction Network and Policy Tree Network, both are deep reinforcement learning architectures offering ways to improve sample complexity and performance on continuous control problems. Furthermore, Policy Tree Network offers the ability to trade extra computation at test time for improved performance via decision-time planning. Performance gains are still observed even in the case of not using decision-time planning(i.e. no extra computation cost relative to the model-free baseline). Our approach integrates a mix between model-free and model-based reinforcement learning. Policy Prediction Network is the first to introduce an implicit model-based approach to Policy Gradient algorithms in continuous action space. Policy Tree Network is the first to leverage an implicit model for decision-time planning in continuous action space. Learning the implicit model is made possible via the empirically justified clipping scheme and depth based objectives. Leveraging the implicit model for decision-time planning is feasible as a result of our tree expansion and backup algorithm. Our experiments are focused on the MuJoCo environments so that they can be compared with similar work done in this area.
Subjects/Keywords: Reinforcement learning
; Machine learning
; Implicit learning
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APA (6th Edition):
Wellmer, Z. W. C. (2019). Building and leveraging implicit models for policy gradient methods. (Thesis). Hong Kong University of Science and Technology. Retrieved from http://repository.ust.hk/ir/Record/1783.1-102375 ; https://doi.org/10.14711/thesis-991012757568703412 ; http://repository.ust.hk/ir/bitstream/1783.1-102375/1/th_redirect.html
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Wellmer, Zachary William CSE. “Building and leveraging implicit models for policy gradient methods.” 2019. Thesis, Hong Kong University of Science and Technology. Accessed March 01, 2021.
http://repository.ust.hk/ir/Record/1783.1-102375 ; https://doi.org/10.14711/thesis-991012757568703412 ; http://repository.ust.hk/ir/bitstream/1783.1-102375/1/th_redirect.html.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Wellmer, Zachary William CSE. “Building and leveraging implicit models for policy gradient methods.” 2019. Web. 01 Mar 2021.
Vancouver:
Wellmer ZWC. Building and leveraging implicit models for policy gradient methods. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2019. [cited 2021 Mar 01].
Available from: http://repository.ust.hk/ir/Record/1783.1-102375 ; https://doi.org/10.14711/thesis-991012757568703412 ; http://repository.ust.hk/ir/bitstream/1783.1-102375/1/th_redirect.html.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Wellmer ZWC. Building and leveraging implicit models for policy gradient methods. [Thesis]. Hong Kong University of Science and Technology; 2019. Available from: http://repository.ust.hk/ir/Record/1783.1-102375 ; https://doi.org/10.14711/thesis-991012757568703412 ; http://repository.ust.hk/ir/bitstream/1783.1-102375/1/th_redirect.html
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Notre Dame
8.
WonJae Shin.
Learning a Recursive Center-Embedding Rule and the Role of
Test Method and Feedback</h1>.
Degree: Psychology, 2014, University of Notre Dame
URL: https://curate.nd.edu/show/nc580k24h9w
► Language is a quintessential and complex human skill. One reflection is the ability to produce and understand an infinite number of new sentences (i.e.,…
(more)
▼ Language is a quintessential and complex
human skill. One reflection is the ability to produce and
understand an infinite number of new sentences (i.e., novel
sequences of words) that convey richly detailed messages. This
ability is ascribed to a grammatical rule system that specifies how
categories of words are combined into phrases and clauses. A major
question in language acquisition is how children induce the
grammatical rules from experience with instances of it and without
explicit correction on their production of erroneous instances. The
“problem of induction” is that the grammatical rules are recursive,
allowing phrases and clauses to be embedded in other phrases and
clauses. For example, a recursive center-embedding rule permits a
sentence such as, The dog that the girls that the boy likes feed
barks. Numerous studies have investigated the rule induction
process using the Artificial Grammar
Learning (AGL) paradigm, which
involves presenting sequences of meaningless elements generated
from a rule to children or adult participants and testing their
learning by having them judge the grammaticality of correct and
incorrect novel sequences. Although several previous AGL studies
showed that adults were unable to induce a center-embedding rule,
Lai and Poletiek (2011) demonstrated successful induction if the
learning experience began with simple sequences with no embedding
and then progressed to sequences with one- and then two embeddings.
However, a flaw in Lai and Poletiek’s test sequences undermined the
validity of their findings. Experiment 1 corrected this flaw, and
replicated Lai and Poletiek’s results, thereby providing stronger
evidence that progressive
learning experience supports the
induction of the center-embedding rule. Experiment 2 provided
further evidence by replacing the grammaticality judgment test with
one that required the participants to produce completions to
initial portions of sequences. Finally, Experiment 3 showed no
learning when feedback was eliminated from the grammaticality
judgment test, but
learning still occurred with the completion
test. Examination of individual performance on the completion tests
in Experiments 2 and 3 revealed that not all participants induced
the rule, and those that did showed a consistent trajectory: They
first mastered the dependencies between the different categories of
elements in the simple sequences, and then hypothesized a symmetric
embedding rule before inducing the correct center-embedding rule.
Thus, the results provide substantive insight into the conditions
that enable the induction of a recursive grammar rule and the
utility of the completion test for investigating the
learning
process.
Advisors/Committee Members: Jill Lany, Committee Member, Sidney DMello, Committee Member, Kathleen M. Eberhard, Committee Chair, Michael Villano, Committee Member.
Subjects/Keywords: Statistical learning; Learning; Grammar; Rule learning; Recursion
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APA (6th Edition):
Shin, W. (2014). Learning a Recursive Center-Embedding Rule and the Role of
Test Method and Feedback</h1>. (Thesis). University of Notre Dame. Retrieved from https://curate.nd.edu/show/nc580k24h9w
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Shin, WonJae. “Learning a Recursive Center-Embedding Rule and the Role of
Test Method and Feedback</h1>.” 2014. Thesis, University of Notre Dame. Accessed March 01, 2021.
https://curate.nd.edu/show/nc580k24h9w.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Shin, WonJae. “Learning a Recursive Center-Embedding Rule and the Role of
Test Method and Feedback</h1>.” 2014. Web. 01 Mar 2021.
Vancouver:
Shin W. Learning a Recursive Center-Embedding Rule and the Role of
Test Method and Feedback</h1>. [Internet] [Thesis]. University of Notre Dame; 2014. [cited 2021 Mar 01].
Available from: https://curate.nd.edu/show/nc580k24h9w.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Shin W. Learning a Recursive Center-Embedding Rule and the Role of
Test Method and Feedback</h1>. [Thesis]. University of Notre Dame; 2014. Available from: https://curate.nd.edu/show/nc580k24h9w
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Toronto
9.
Makhzani, Alireza.
Unsupervised Representation Learning with Autoencoders.
Degree: PhD, 2018, University of Toronto
URL: http://hdl.handle.net/1807/89800
► Despite the recent progress in machine learning and deep learning, unsupervised learning still remains a largely unsolved problem. It is widely recognized that unsupervised learning…
(more)
▼ Despite the recent progress in machine
learning and deep
learning, unsupervised
learning still remains a largely unsolved problem. It is widely recognized that unsupervised
learning algorithms that can learn useful representations are needed for solving problems with limited label information. In this thesis, we study the problem of
learning unsupervised representations using autoencoders, and propose regularization techniques that enable autoencoders to learn useful representations of data in unsupervised and semi-supervised settings. First, we exploit sparsity as a generic prior on the representations of autoencoders and propose sparse autoencoders that can learn sparse representations with very fast inference processes, making them well-suited to large problem sizes where conventional sparse coding algorithms cannot be applied. Next, we study autoencoders from a probabilistic perspective and propose generative autoencoders that use a generative adversarial network (GAN) to match the distribution of the latent code of the autoencoder with a pre-defined prior. We show that these generative autoencoders can learn posterior approximations that are more expressive than tractable densities often used in variational inference. We demonstrate the performance of developed methods of this thesis on real world image datasets and show their applications in generative modeling, clustering, semi-supervised classification and dimensionality reduction.
Advisors/Committee Members: Frey, Brendan, Electrical and Computer Engineering.
Subjects/Keywords: Deep Learning; Machine Learning; Unsupervised Learning; 0984
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APA (6th Edition):
Makhzani, A. (2018). Unsupervised Representation Learning with Autoencoders. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/89800
Chicago Manual of Style (16th Edition):
Makhzani, Alireza. “Unsupervised Representation Learning with Autoencoders.” 2018. Doctoral Dissertation, University of Toronto. Accessed March 01, 2021.
http://hdl.handle.net/1807/89800.
MLA Handbook (7th Edition):
Makhzani, Alireza. “Unsupervised Representation Learning with Autoencoders.” 2018. Web. 01 Mar 2021.
Vancouver:
Makhzani A. Unsupervised Representation Learning with Autoencoders. [Internet] [Doctoral dissertation]. University of Toronto; 2018. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1807/89800.
Council of Science Editors:
Makhzani A. Unsupervised Representation Learning with Autoencoders. [Doctoral Dissertation]. University of Toronto; 2018. Available from: http://hdl.handle.net/1807/89800
University of Waterloo
10.
Lin, Ying-Hsin.
Characterizing Colour-Word Contingency Learning.
Degree: 2015, University of Waterloo
URL: http://hdl.handle.net/10012/9279
► Contingencies are constantly found in everyday situations and humans are extraordinarily adept at learning them, whether implicitly or explicitly. The learning of contingencies can benefit…
(more)
▼ Contingencies are constantly found in everyday situations and humans are extraordinarily adept at learning them, whether implicitly or explicitly. The learning of contingencies can benefit behaviour in many ways, some subtle and some much more apparent. But the question of how we can improve the efficiency of our performance and what factors influence a person’s learning of contingencies remains relatively limited in exploration. In this dissertation, I use a simple contingency learning paradigm, the colour-word contingency paradigm (Schmidt, Crump, Cheesman, & Besner, 2007) to address three issues regarding the learning of contingencies: (1) Is there a cost in performance—in addition to a benefit—when learning contingencies?; (2) Does instance frequency play a role in the learning of contingencies?; and (3) Can people use higher-order associations such as semantic associations in learning contingencies? In the first series of experiments (Experiments 1-4), I found that although there is benefit in responding to events with high contingencies, there also is a cost to events with low contingencies. In the second series (Experiments 5-7), I showed that event frequency does play a role in contingency learning but that role is dependent on factors such as whether there is perfect contingency between events and how balanced the frequencies are in the high- and low-contingency conditions. In the third series (Experiments 8-10), I observed that people are capable of using higher-order associations—in this case, semantics—to guide the efficiency of their responding, however the use of such associations is dependent on whether the task encourages their use. This dissertation thereby addresses three issues where research has been quite tentative and, in some cases, the present findings are in contrast to the current literature. The overall goal was for this research to contribute to the growing literature on fundamental factors that can influence how we acquire contingencies, and to help us understand how we optimize our efficiency in learning, even when that learning occurs without awareness.
Subjects/Keywords: psychology; cognition; learning; implicit learning; contingency learning
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APA (6th Edition):
Lin, Y. (2015). Characterizing Colour-Word Contingency Learning. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/9279
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Lin, Ying-Hsin. “Characterizing Colour-Word Contingency Learning.” 2015. Thesis, University of Waterloo. Accessed March 01, 2021.
http://hdl.handle.net/10012/9279.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Lin, Ying-Hsin. “Characterizing Colour-Word Contingency Learning.” 2015. Web. 01 Mar 2021.
Vancouver:
Lin Y. Characterizing Colour-Word Contingency Learning. [Internet] [Thesis]. University of Waterloo; 2015. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/10012/9279.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Lin Y. Characterizing Colour-Word Contingency Learning. [Thesis]. University of Waterloo; 2015. Available from: http://hdl.handle.net/10012/9279
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Georgia
11.
Hong, Sunjoo.
Factors and issues supporting learning communities among distance learners.
Degree: 2014, University of Georgia
URL: http://hdl.handle.net/10724/21531
► The purpose of this study was to investigate sources and processes that impacted community building among distance learners enrolled in an online cohort program within…
(more)
▼ The purpose of this study was to investigate sources and processes that impacted community building among distance learners enrolled in an online cohort program within the context of higher education. Sub-purposes were threefold. First, this
study identified the existence of a community in the online cohort program. Next, it identified factors and issues that supported the creation and sustained the community in the online cohort. A third purpose was to describe the processes of individual
participants’ involvement in the community throughout the period of the cohort program. A qualitative case study design was appropriate, given the research questions. The case was an online, four semester-long, non-degree program based on a cohort model
offered in a large southeastern university. Data were primarily gathered through two phases of open-ended questionnaires to self-selected participants. Another source of data was the postings on the course bulletin boards made by the four primary
participants during the last three semesters. Data were inductively analyzed and interpreted searching for themes and patterns. Those indicators that supported the development of a community extracted from related literature were also found in the data.
These indicators included shared goals and practice, support, and feelings of belonging. In this study, the students of the cohort shared the communal goal of pursuing additional credential to their education certification. Through interaction,
engagement, and alignment, the students showed that they supported each other’s learning, developed shared practice, and felt a sense of belonging. Community building in this online cohort was a result of the interaction of students, instructors, and
circumstances of this particular program. Interaction, engagement, and alignment among the students; assistance and facilitation by the instructors; course structure; and the use of a cohort model appeared to have had an impact on community building.
Although the students belonged to the same community, they revealed diverse experiences in it. They engaged in the community differently depending on their individual needs, desire, and situations. The individual students reported different concepts of a
community, different levels of involvement in the community, and different way of connecting with others in the community.
Subjects/Keywords: Online learning; Distance learning; Learning communities; Community
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APA (6th Edition):
Hong, S. (2014). Factors and issues supporting learning communities among distance learners. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/21531
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Hong, Sunjoo. “Factors and issues supporting learning communities among distance learners.” 2014. Thesis, University of Georgia. Accessed March 01, 2021.
http://hdl.handle.net/10724/21531.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Hong, Sunjoo. “Factors and issues supporting learning communities among distance learners.” 2014. Web. 01 Mar 2021.
Vancouver:
Hong S. Factors and issues supporting learning communities among distance learners. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/10724/21531.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Hong S. Factors and issues supporting learning communities among distance learners. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/21531
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Texas – Austin
12.
Shah, Rishi Alpesh.
Deep R learning for continual area sweeping.
Degree: MSin Computational Science, Engineering, and Mathematics, Computational Science, Engineering, and Mathematics, 2019, University of Texas – Austin
URL: http://dx.doi.org/10.26153/tsw/8201
► In order to maintain robustness, autonomous robots need to constantly update their knowledge of the environment, which can be expensive when they are deployed in…
(more)
▼ In order to maintain robustness, autonomous robots need to constantly update their knowledge of the environment, which can be expensive when they are deployed in large, dynamic spaces. The continual area sweeping task formalizes the problem of a robot continually patrolling an area in a non-uniform way in order to efficiently use travel time. However, the existing problem formulation makes strong assumptions about the environment, and to date only a sub-optimal greedy approach has been proposed. We generalize the continual area sweeping formulation to include fewer environmental constraints, and propose a novel reinforcement
learning approach. We evaluate our approach in an abstract simulation and in a high fidelity Gazebo simulation, which shows significant improvement upon the initial approach in general settings
Advisors/Committee Members: Dawson, Clinton N. (advisor).
Subjects/Keywords: Machine learning; Reinforcement learning; Robotics; Robot learning
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APA (6th Edition):
Shah, R. A. (2019). Deep R learning for continual area sweeping. (Masters Thesis). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/8201
Chicago Manual of Style (16th Edition):
Shah, Rishi Alpesh. “Deep R learning for continual area sweeping.” 2019. Masters Thesis, University of Texas – Austin. Accessed March 01, 2021.
http://dx.doi.org/10.26153/tsw/8201.
MLA Handbook (7th Edition):
Shah, Rishi Alpesh. “Deep R learning for continual area sweeping.” 2019. Web. 01 Mar 2021.
Vancouver:
Shah RA. Deep R learning for continual area sweeping. [Internet] [Masters thesis]. University of Texas – Austin; 2019. [cited 2021 Mar 01].
Available from: http://dx.doi.org/10.26153/tsw/8201.
Council of Science Editors:
Shah RA. Deep R learning for continual area sweeping. [Masters Thesis]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/8201
University of Illinois – Urbana-Champaign
13.
Benson, Christopher Edward.
Improving cache replacement policy using deep reinforcement learning.
Degree: MS, Computer Science, 2018, University of Illinois – Urbana-Champaign
URL: http://hdl.handle.net/2142/102858
► This thesis explores the use of reinforcement learning approaches to improve replacement policies of caches. In today's internet, caches play a vital role in improving…
(more)
▼ This thesis explores the use of reinforcement
learning approaches to improve replacement policies of caches. In today's internet, caches play a vital role in improving performance of data transfers and load speeds. From video streaming to information retrieval from databases, caches allow applications to function more quickly and efficiently. A cache's replacement policy plays a major role in determining the cache's effectiveness and performance. The replacement policy is an algorithm that chooses which piece of data in the cache should be evicted when the cache becomes full and new elements are requested. In computer systems today, most caches use simple heuristic-based policies. Currently used policies are effective but are still far from optimal. Using more optimal cache replacement policies could dramatically improve internet performance and reduce database costs for many industry-based companies.
This research examines
learning more optimal replacement policies using reinforcement
learning. In reinforcement
learning, an agent learns to take optimal actions given information about an environment and a reward signal. In this work, deep reinforcement
learning algorithms are trained to learn optimal cache replacement policies using a simulated cache environment and database access traces. This research presents the idea of using index-based cache access histories as input data for the reinforcement
learning algorithms instead of content-based input. Several approaches are explored including value-based algorithms and policy gradient algorithms. The work presented here also explores the idea of using imitation
learning algorithms to mimic optimal cache replacement policies. The algorithms are tested on several different cache sizes and data access patterns to show that these learned policies can outperform currently used replacement policies in a variety of settings.
Advisors/Committee Members: Peng, Jian (advisor).
Subjects/Keywords: Reinforcement Learning; Machine Learning; Deep Learning
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APA (6th Edition):
Benson, C. E. (2018). Improving cache replacement policy using deep reinforcement learning. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/102858
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Benson, Christopher Edward. “Improving cache replacement policy using deep reinforcement learning.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed March 01, 2021.
http://hdl.handle.net/2142/102858.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Benson, Christopher Edward. “Improving cache replacement policy using deep reinforcement learning.” 2018. Web. 01 Mar 2021.
Vancouver:
Benson CE. Improving cache replacement policy using deep reinforcement learning. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/2142/102858.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Benson CE. Improving cache replacement policy using deep reinforcement learning. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/102858
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Oregon State University
14.
Proper, Scott.
Scaling multiagent reinforcement learning.
Degree: PhD, Computer Science, 2009, Oregon State University
URL: http://hdl.handle.net/1957/13662
► Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity or "outcome space" explosion. Multiagent…
(more)
▼ Reinforcement
learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high
stochasticity or "outcome space" explosion. Multiagent domains are particularly susceptible to these problems. This thesis describes ways to mitigate these curses in several different multiagent domains, including real-time delivery of products using multiple vehicles with stochastic demands, a multiagent predator-prey domain, and a domain based on a real-time strategy game.
To mitigate the problem of state-space explosion, this thesis present several approaches that mitigate each of these curses. "Tabular linear functions" (TLFs) are introduced that generalize tile-coding and linear value functions and allow
learning of complex nonlinear functions in high-dimensional state-spaces. It is also shown how to adapt TLFs to relational domains, creating a "lifted" version called relational templates.
To mitigate the problem of action-space explosion, the replacement of complete joint action space search with a form of hill climbing is described. To mitigate the problem of outcome space explosion, a more efficient calculation of the expected value of the next state is shown, and two real-time dynamic programming algorithms based on afterstates, ASH-
learning and ATR-
learning, are introduced.
Lastly, two approaches that scale by treating a multiagent domain as being formed of several coordinating agents are presented. "Multiagent H-
learning" and "Multiagent ASH-
learning" are described, where coordination is achieved through a method called "serial coordination". This technique has the benefit of addressing each of the three curses of dimensionality simultaneously by reducing the space of states and actions each local agent must consider.
The second approach to multiagent coordination presented is "assignment-based decomposition", which divides the action selection step into an assignment phase and a primitive action selection step. Like the multiagent approach, assignment-based decomposition addresses all three curses of dimensionality simultaneously by reducing the space of states and actions each group of agents must consider. This method is capable of much more sophisticated coordination.
Experimental results are presented which show successful application of all methods described. These results demonstrate that the scaling techniques described in this thesis can greatly mitigate the three curses of dimensionality and allow solutions for multiagent domains to scale to large numbers of agents, and complex state and outcome spaces.
Advisors/Committee Members: Tadepalli, Prasad (advisor), Dietterich, Thomas (committee member).
Subjects/Keywords: Reinforcement learning; Reinforcement learning
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APA ·
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APA (6th Edition):
Proper, S. (2009). Scaling multiagent reinforcement learning. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/13662
Chicago Manual of Style (16th Edition):
Proper, Scott. “Scaling multiagent reinforcement learning.” 2009. Doctoral Dissertation, Oregon State University. Accessed March 01, 2021.
http://hdl.handle.net/1957/13662.
MLA Handbook (7th Edition):
Proper, Scott. “Scaling multiagent reinforcement learning.” 2009. Web. 01 Mar 2021.
Vancouver:
Proper S. Scaling multiagent reinforcement learning. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1957/13662.
Council of Science Editors:
Proper S. Scaling multiagent reinforcement learning. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/13662
Oregon State University
15.
Vatturi, Pavan Kumar.
Rare category detection using hierarchical mean shift.
Degree: MS, Computer Science, 2009, Oregon State University
URL: http://hdl.handle.net/1957/10191
► Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant…
(more)
▼ Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine
learning that can address this issue using a "human-in-the-loop" approach. In this interactive setting, the algorithm asks the user to label a query data point under an existing category or declare the query data point to belong to a previously undiscovered category. The goal of category detection is to discover all the categories in the data in as few queries as possible. In a data set with imbalanced categories, the main challenge is in identifying the rare categories or anomalies; hence, the task is often referred to as rare category detection.
We present a new approach to rare category detection using a hierarchical mean shift procedure. In our approach, a hierarchy is created by repeatedly applying mean shift with increasing bandwidth on the entire data set. This hierarchy allows us to identify anomalies in the data set at different scales, which are then posed as queries to the user. The main advantage of this methodology over existing approaches is that it does not require any knowledge of the data set properties such as the total number of classes or the prior probabilities of the classes. Results on real-world data sets show that our hierarchical mean shift approach performs consistently better than previous techniques.
Advisors/Committee Members: Wong, Weng-Keen (advisor), Fern, Alan (committee member).
Subjects/Keywords: machine learning; Machine learning
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APA (6th Edition):
Vatturi, P. K. (2009). Rare category detection using hierarchical mean shift. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/10191
Chicago Manual of Style (16th Edition):
Vatturi, Pavan Kumar. “Rare category detection using hierarchical mean shift.” 2009. Masters Thesis, Oregon State University. Accessed March 01, 2021.
http://hdl.handle.net/1957/10191.
MLA Handbook (7th Edition):
Vatturi, Pavan Kumar. “Rare category detection using hierarchical mean shift.” 2009. Web. 01 Mar 2021.
Vancouver:
Vatturi PK. Rare category detection using hierarchical mean shift. [Internet] [Masters thesis]. Oregon State University; 2009. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1957/10191.
Council of Science Editors:
Vatturi PK. Rare category detection using hierarchical mean shift. [Masters Thesis]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/10191
Oregon State University
16.
Hao, Guohua.
Revisiting output coding for sequential supervised learning.
Degree: MS, Computer Science, 2009, Oregon State University
URL: http://hdl.handle.net/1957/10897
► Markov models are commonly used for joint inference of label sequences. Unfortunately, inference scales quadratically in the number of labels, which is problematic for training…
(more)
▼ Markov models are commonly used for joint inference of label sequences. Unfortunately, inference scales quadratically in the number of labels, which is problematic for training methods where inference is repeatedly preformed and is the primary computational bottleneck for large label sets. Recent work has used output coding to address this issue by converting a problem with many labels to a set of problems with binary labels. Models were independently trained for each binary problem, at a much reduced computational cost, and then combined for joint inference over the original labels. Here we revisit this idea and show through experiments on synthetic and benchmark data sets that the approach can perform poorly when it is critical to explicitly capture the Markovian transition structure of the large-label problem. We then describe a simple cascade-training approach and show that it can improve performance on such problems with negligible computational overhead.
Advisors/Committee Members: Fern, Alan (advisor), Dietterich, Thomas (committee member).
Subjects/Keywords: ECOC; Supervised learning (Machine learning)
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APA ·
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APA (6th Edition):
Hao, G. (2009). Revisiting output coding for sequential supervised learning. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/10897
Chicago Manual of Style (16th Edition):
Hao, Guohua. “Revisiting output coding for sequential supervised learning.” 2009. Masters Thesis, Oregon State University. Accessed March 01, 2021.
http://hdl.handle.net/1957/10897.
MLA Handbook (7th Edition):
Hao, Guohua. “Revisiting output coding for sequential supervised learning.” 2009. Web. 01 Mar 2021.
Vancouver:
Hao G. Revisiting output coding for sequential supervised learning. [Internet] [Masters thesis]. Oregon State University; 2009. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1957/10897.
Council of Science Editors:
Hao G. Revisiting output coding for sequential supervised learning. [Masters Thesis]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/10897
Oregon State University
17.
Bao, Xinlong.
Applying machine learning for prediction, recommendation, and integration.
Degree: PhD, Computer Science, 2009, Oregon State University
URL: http://hdl.handle.net/1957/12549
► This dissertation explores the idea of applying machine learning technologies to help computer users find information and better organize electronic resources, by presenting the research…
(more)
▼ This dissertation explores the idea of applying machine
learning technologies to help computer users find information and better organize electronic resources, by presenting the research work conducted in the following three applications: FolderPredictor, Stacking Recommendation Engines, and Integrating
Learning and Reasoning.
FolderPredictor is an intelligent desktop software tool that helps the user quickly locate files on the computer. It predicts the file folder that the user will access next by applying machine
learning algorithms to the user's file access history. The predicted folders are presented in existing Windows GUIs, so that the user's cost for
learning new interactions is minimized. Multiple prediction algorithms are introduced and their performance is examined in two user studies.
Recommender systems are one of the most popular means of assisting internet users in finding useful online information. The second part of this dissertation presents a novel way of building hybrid recommender systems by applying the idea of Stacking from ensemble
learning. Properties of the input users/items, called runtime metrics, are employed as additional meta features to improve performance. The resulting system, called STREAM, outperforms each component engine and a static linear hybrid system in a movie recommendation problem.
Many desktop assistant systems help users better organize their electronic resources by incorporating machine
learning components (e.g., classifiers) to make intelligent predictions. The last part of this dissertation addresses the problem of how to improve the performance of these
learning components, by integrating
learning and reasoning through Markov logic. Through an inference engine called the PCE, multiple classifiers are integrated via a process called relational co-training that improves the performance of each classifier based on information propagated from other classifiers.
Advisors/Committee Members: Dietterich, Thomas G. (advisor), Bergman, Lawrence (committee member).
Subjects/Keywords: machine learning; Machine learning
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APA ·
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to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Bao, X. (2009). Applying machine learning for prediction, recommendation, and integration. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/12549
Chicago Manual of Style (16th Edition):
Bao, Xinlong. “Applying machine learning for prediction, recommendation, and integration.” 2009. Doctoral Dissertation, Oregon State University. Accessed March 01, 2021.
http://hdl.handle.net/1957/12549.
MLA Handbook (7th Edition):
Bao, Xinlong. “Applying machine learning for prediction, recommendation, and integration.” 2009. Web. 01 Mar 2021.
Vancouver:
Bao X. Applying machine learning for prediction, recommendation, and integration. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1957/12549.
Council of Science Editors:
Bao X. Applying machine learning for prediction, recommendation, and integration. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/12549
University of Namibia
18.
Nyambe Kamwi John.
Teacher educators' interpretation and practice of learner-centred pedagogy
.
Degree: 2008, University of Namibia
URL: http://hdl.handle.net/11070/559
► The objective of this study was to understand how teacher educators in a Namibian college of education interpret and practice the learner-centred pedagogy underpinning the…
(more)
▼ The objective of this study was to understand how teacher educators in a Namibian college of education interpret and practice the learner-centred pedagogy underpinning the Basic Education Teachers Diploma (BETD) program. In order to achieve this objective, a case study approach was adopted, qualitative-interpretive in orientation and drawing upon interviews, naturalistic non-participant observation and document analysis. Bernstein's theory of pedagogy - in particular his notion ofrecontextualization - offered ideas and concepts that were used to generate and analyse data. The data indicated that, at the level of description, teacher educators interpreted leamercentred pedagogy as a pedagogic practice based on weak rules of regulative discourse, or a weak power relation between themselves and their student teachers. The weakening of the rules of regulative discourse and the waning of educator authority were indicated in the interview narratives, which evoked a pedagogic context characterized by a repositioning of the student teacher from the margins to the centre of the classroom, where he or she enjoyed a more active and visible pedagogic position. Contrary to the dis empowering dynamic within classroom practice under the apartheid dispensation, the repositioning of the student teacher suggested a shift of power towards him or her. Similarly, the identification of the teacher educator as afacilitator, which featured prominently in the interview narratives, further suggested a weakening or diminishing of the pedagogic authority of the teacher educator. With regard to rules pertaining to the instructional discourse, the data revealed an interpretation of leamer-centred pedagogy as a pedagogic practice based on strong framing over the selection of discourses, weak framing over pacing, and strong framing over sequencing and criteria for evaluation. When correlated with the interview data, the data generated through lesson observation and teacher educator prepared documents such as lesson plans revealed a disjuncture between teacher educators' ideas about leamer-centred pedagogy and their practice of it. Contrary to the interviews, lesson observation data revealed that teacher educators implemented leamer-centred pedagogy as a pedagogic practice based on strong internal framing over rules of the regulative discourse. Data further indicated strong internal framing over the selection, sequencing, pacing and evaluation. The study concluded that while some teacher educators could produce an accurate interpretation oflearner-centred pedagogy at the level of description, most of them did not do so at the level of practice. Findings revealed structural and personal-psychological factors that constrained teacher educators' recontextualization of the new pedagogy. A narrow understanding of leamercentred pedagogy that concentrated only on changing teacher educators' pedagogical approaches from teacher-centred to learner-centred, while ignoring structural and systematic factors, tended to dominate not only the interview narratives…
Advisors/Committee Members: Smith Clive (advisor), Wilmot Dianne (advisor).
Subjects/Keywords: Student- centred learning
;
Learning
;
Teaching
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APA (6th Edition):
John, N. K. (2008). Teacher educators' interpretation and practice of learner-centred pedagogy
. (Thesis). University of Namibia. Retrieved from http://hdl.handle.net/11070/559
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
John, Nyambe Kamwi. “Teacher educators' interpretation and practice of learner-centred pedagogy
.” 2008. Thesis, University of Namibia. Accessed March 01, 2021.
http://hdl.handle.net/11070/559.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
John, Nyambe Kamwi. “Teacher educators' interpretation and practice of learner-centred pedagogy
.” 2008. Web. 01 Mar 2021.
Vancouver:
John NK. Teacher educators' interpretation and practice of learner-centred pedagogy
. [Internet] [Thesis]. University of Namibia; 2008. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/11070/559.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
John NK. Teacher educators' interpretation and practice of learner-centred pedagogy
. [Thesis]. University of Namibia; 2008. Available from: http://hdl.handle.net/11070/559
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Oregon State University
19.
Mehta, Neville.
Hierarchical structure discovery and transfer in sequential decision problems.
Degree: PhD, Computer Science, 2011, Oregon State University
URL: http://hdl.handle.net/1957/25199
► Acting intelligently to efficiently solve sequential decision problems requires the ability to extract hierarchical structure from the underlying domain dynamics, exploit it for optimal or…
(more)
▼ Acting intelligently to efficiently solve sequential decision problems requires the ability to extract hierarchical structure from the underlying domain dynamics, exploit it for optimal or near-optimal decision-making, and transfer it to related problems instead of solving every problem in isolation. This dissertation makes three contributions toward this goal.
The first contribution is the introduction of two frameworks for the transfer of hierarchical structure in sequential decision problems. The MASH framework facilitates transfer among multiple agents coordinating within a domain. The VRHRL framework allows an agent to transfer its knowledge across a family of domains that share the same transition dynamics but have differing reward dynamics. Both MASH and VRHRL are validated empirically in large domains and the results demonstrate significant speedup in the solutions due to transfer.
The second contribution is a new approach to the discovery of hierarchical structure in sequential decision problems. HI-MAT leverages action models to analyze the relevant dependencies in a hierarchically-generated trajectory and it discovers hierarchical structure that transfers to all problems whose actions share the same relevant dependencies as the single source problem. HierGen advances HI-MAT by
learning simple action models, leveraging these models to analyze non-hierarchically-generated trajectories from multiple source problems in a robust causal fashion, and discovering hierarchical structure that transfers to all problems whose actions share the same causal dependencies as those in the source problems. Empirical evaluations in multiple domains demonstrate that the discovered hierarchical structures are comparable to manually-designed structures in quality and performance.
Action models are essential to hierarchical structure discovery and other aspects of intelligent behavior. The third contribution of this dissertation is the introduction of two general frameworks for
learning action models in sequential decision problems. In the MBP framework,
learning is user-driven; in the PLEX framework, the learner generates its own problems. The frameworks are formally analyzed and reduced to concept
learning with one-sided error. A general action-modeling language is shown to be efficiently learnable in both frameworks.
Advisors/Committee Members: Tadepalli, Prasad (advisor), Dietterich, Thomas (committee member).
Subjects/Keywords: hierarchical reinforcement learning; Reinforcement learning
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APA (6th Edition):
Mehta, N. (2011). Hierarchical structure discovery and transfer in sequential decision problems. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/25199
Chicago Manual of Style (16th Edition):
Mehta, Neville. “Hierarchical structure discovery and transfer in sequential decision problems.” 2011. Doctoral Dissertation, Oregon State University. Accessed March 01, 2021.
http://hdl.handle.net/1957/25199.
MLA Handbook (7th Edition):
Mehta, Neville. “Hierarchical structure discovery and transfer in sequential decision problems.” 2011. Web. 01 Mar 2021.
Vancouver:
Mehta N. Hierarchical structure discovery and transfer in sequential decision problems. [Internet] [Doctoral dissertation]. Oregon State University; 2011. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1957/25199.
Council of Science Editors:
Mehta N. Hierarchical structure discovery and transfer in sequential decision problems. [Doctoral Dissertation]. Oregon State University; 2011. Available from: http://hdl.handle.net/1957/25199
Oregon State University
20.
Liu, Liping.
Machine Learning Methods for Computational Sustainability.
Degree: PhD, Computer Science, 2016, Oregon State University
URL: http://hdl.handle.net/1957/59159
► Maintaining the sustainability of the earth’s ecosystems has attracted much attention as these ecosystems are facing more and more pressure from human activities. Machine learning…
(more)
▼ Maintaining the sustainability of the earth’s ecosystems has attracted much attention as these
ecosystems are facing more and more pressure from human activities. Machine
learning can
play an important role in promoting sustainability as a large amount of data is being collected
from ecosystems. There are at least three important and representative issues in the study of
sustainability: detecting the presence of species, modeling the distribution of species, and protecting
endangered species. For these three issues, this thesis selects three typical problems as
the main focus and studies these problems with different machine
learning techniques. Specifically,
this thesis investigates the problem of detecting bird species from bird song recordings,
the problem of modeling migrating birds at the population level, and the problem of designing a
conservation area for an endangered species. First, this thesis models the problem of bird song
classification as a weakly-supervised
learning problem and develops a probabilistic classification
model for the
learning problem. The thesis also analyzes the learnability of the superset label
learning problem to determine conditions under which one can learn a good classifier from the
training data. Second, the thesis models bird migration with a probabilistic graphical model at
the population level using a Collective Graphical Model (CGM). The thesis proposes a Gaussian
approximation to significantly improve the inference efficiency of the model. Theoretical results
show that the proposed Gaussian approximation is correct and can be calculated efficiently.
Third, the thesis studies a typical reserve design problem with a novel formulation of transductive
classification. Then the thesis solves the formulation with two optimization algorithms. The
learning techniques in this thesis are general and can also be applied to many other machine
learning problems.
Advisors/Committee Members: Dietterich, Thomas (advisor), Fern, Xiaoli Z. (committee member).
Subjects/Keywords: machine learning; Machine learning
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APA ·
Chicago ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Liu, L. (2016). Machine Learning Methods for Computational Sustainability. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/59159
Chicago Manual of Style (16th Edition):
Liu, Liping. “Machine Learning Methods for Computational Sustainability.” 2016. Doctoral Dissertation, Oregon State University. Accessed March 01, 2021.
http://hdl.handle.net/1957/59159.
MLA Handbook (7th Edition):
Liu, Liping. “Machine Learning Methods for Computational Sustainability.” 2016. Web. 01 Mar 2021.
Vancouver:
Liu L. Machine Learning Methods for Computational Sustainability. [Internet] [Doctoral dissertation]. Oregon State University; 2016. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1957/59159.
Council of Science Editors:
Liu L. Machine Learning Methods for Computational Sustainability. [Doctoral Dissertation]. Oregon State University; 2016. Available from: http://hdl.handle.net/1957/59159
Georgia Tech
21.
Bullard, Kalesha.
Managing learning interactions for collaborative robot learning.
Degree: PhD, Interactive Computing, 2019, Georgia Tech
URL: http://hdl.handle.net/1853/62294
► Robotic assistants should be able to actively engage their human partner(s) to generalize knowledge about relevant tasks within their shared environment. Yet a key challenge…
(more)
▼ Robotic assistants should be able to actively engage their human partner(s) to generalize knowledge about relevant tasks within their shared environment. Yet a key challenge is not all human partners will be proficient at teaching; furthermore, humans should not be held accountable for tracking a robot’s knowledge over time in a dynamically changing environment, across multiple tasks. Thus, it is important to enable these interactive robots to characterize their own uncertainty and equip them with an information gathering policy for asking the appropriate questions of their human partners to resolve that uncertainty. In this way, the robot shares the responsibility in guiding its own
learning process and is a collaborator in the
learning. Additionally, given the robot requires some tutelage from its partner, awareness of constraints on the teacher’s time and cognitive resources available for devoting to the interaction could help the agent to use the time allotted more wisely. This thesis examines the problem of enabling a robotic agent to leverage structured interaction with a human partner for acquiring concepts relevant to a task it must later perform. To equip the agent with the desired concept knowledge, we first explore the paradigm of
Learning from Demonstration for the acquisition of (1) training instances as examples of task-relevant concepts and (2) informative features for appropriately representing and discriminating between task-relevant concepts. Given empirical evidence that a human partner can be helpful to the agent in solving the concept
learning problem, we subsequently investigate the design of algorithms that enable the robot learner to autonomously manage interaction with its human partner, using a questioning policy to actively gather both instance and feature information. This thesis seeks to investigate the following hypothesis: In the context of robot
learning from human demonstrations in changeable and resource-constrained environments, enabling the robot to actively elicit multiple types of information through questions, and to reason about what question to ask and when, leads to improved
learning performance.
Advisors/Committee Members: Chernova, Sonia (advisor), Isbell, Charles (committee member), Christensen, Henrik I. (committee member), Mataric, Maja (committee member), Thomaz, Andrea L. (committee member).
Subjects/Keywords: Interactive robot learning; Active learning
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APA (6th Edition):
Bullard, K. (2019). Managing learning interactions for collaborative robot learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62294
Chicago Manual of Style (16th Edition):
Bullard, Kalesha. “Managing learning interactions for collaborative robot learning.” 2019. Doctoral Dissertation, Georgia Tech. Accessed March 01, 2021.
http://hdl.handle.net/1853/62294.
MLA Handbook (7th Edition):
Bullard, Kalesha. “Managing learning interactions for collaborative robot learning.” 2019. Web. 01 Mar 2021.
Vancouver:
Bullard K. Managing learning interactions for collaborative robot learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1853/62294.
Council of Science Editors:
Bullard K. Managing learning interactions for collaborative robot learning. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62294
Cornell University
22.
Lenz, Ian.
Deep Learning For Robotics.
Degree: PhD, Computer Science, 2016, Cornell University
URL: http://hdl.handle.net/1813/44317
► Robotics faces many unique challenges as robotic platforms move out of the lab and into the real world. In particular, the huge amount of variety…
(more)
▼ Robotics faces many unique challenges as robotic platforms move out of the lab and into the real world. In particular, the huge amount of variety encountered in real-world environments is extremely challenging for existing robotic control algorithms to handle. This necessistates the use of machine
learning algorithms, which are able to learn controls given data. However, most conventional
learning algorithms require hand-designed parameterized models and features, which are infeasible to design for many robotic tasks. Deep
learning algorithms are general non-linear models which are able to learn features directly from data, making them an excellent choice for such robotics applications. However, care must be taken to design deep
learning algorithms and supporting systems appropriate for the task at hand. In this work, I describe two applications of deep
learning algorithms and one application of hardware neural networks to difficult robotics problems. The problems addressed are robotic grasping, food cutting, and aerial robot obstacle avoidance, but the algorithms presented are designed to be generalizable to related tasks.
Advisors/Committee Members: Saxena,Ashutosh (chair), Snavely,Keith Noah (committee member), Manohar,Rajit (committee member), Knepper,Ross A (committee member).
Subjects/Keywords: Robotics; Machine learning; Deep learning
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Manager
APA (6th Edition):
Lenz, I. (2016). Deep Learning For Robotics. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/44317
Chicago Manual of Style (16th Edition):
Lenz, Ian. “Deep Learning For Robotics.” 2016. Doctoral Dissertation, Cornell University. Accessed March 01, 2021.
http://hdl.handle.net/1813/44317.
MLA Handbook (7th Edition):
Lenz, Ian. “Deep Learning For Robotics.” 2016. Web. 01 Mar 2021.
Vancouver:
Lenz I. Deep Learning For Robotics. [Internet] [Doctoral dissertation]. Cornell University; 2016. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1813/44317.
Council of Science Editors:
Lenz I. Deep Learning For Robotics. [Doctoral Dissertation]. Cornell University; 2016. Available from: http://hdl.handle.net/1813/44317
Cornell University
23.
Jiang, Yun.
Hallucinated Humans: Learning Latent Factors To Model 3D Environments.
Degree: PhD, Computer Science, 2015, Cornell University
URL: http://hdl.handle.net/1813/41178
► The ability to correctly reason about human environment is critical for personal robots. For example, if a robot is asked to tidy a room, it…
(more)
▼ The ability to correctly reason about human environment is critical for personal robots. For example, if a robot is asked to tidy a room, it needs to detect object types, such as shoes and books, and then decides where to place them properly. Sometimes being able to anticipate human-environment interactions is also desirable. For example, the robot would not put any object on the chair if it understands that humans would sit on it. The idea of modeling object-object relations has been widely leveraged in many scene understanding applications. For instance, the object found in front of a monitor is more likely to be a keyboard because of the high correlation of the two objects. However, as the objects are designed by humans and for human usage, when we reason about a human environment, we reason about it through an interplay between the environment, objects and humans. For example, the objects, monitor and keyboard, are strongly spatially correlated only because a human types on the keyboard while watching the monitor. The key idea of this thesis is to model environments not only through objects, but also through latent human poses and human-object interactions. We start by designing a generic form of human-object interaction, also referred as 'object affordance'. Human-object relations can thus be quantified through a function of object affordance, human configuration and object con- figuration. Given human poses and object affordances, we can capture the relations among humans, objects and the scene through Conditional Random Fields (CRFs). For scenarios where no humans present, our idea is to still leverage the human-object relations by hallucinating potential human poses. In order to handle the large number of latent human poses and a large variety of their interactions with objects, we present Infinite Latent Conditional Random Field (ILCRF) that models a scene as a mixture of CRFs generated from Dirichlet processes. In each CRF, we model objects and object-object relations as existing nodes and edges, and hidden human poses and human-object relations as latent nodes and edges. ILCRF generatively models the distribution of different CRF structures over these latent nodes and edges. We apply the model to the challenging applications of 3D scene labeling and robotic scene arrangement. In extensive experiments, we show that our model significantly outperforms the state-of-the-art results in both applications. We test our algorithm on a robot for arranging objects in a new scene using the two applications aforementioned. We further extend the idea of hallucinating static human poses to anticipating human activities. We also present
learning-based grasping and placing approaches for low-level manipulation tasks in complimentary to the high-level scene understanding tasks.
Advisors/Committee Members: Saxena,Ashutosh (chair), James,Douglas Leonard (committee member), Kleinberg,Robert David (committee member), Joachims,Thorsten (committee member).
Subjects/Keywords: Robotics; Machine learning; nonparametric learning
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APA (6th Edition):
Jiang, Y. (2015). Hallucinated Humans: Learning Latent Factors To Model 3D Environments. (Doctoral Dissertation). Cornell University. Retrieved from http://hdl.handle.net/1813/41178
Chicago Manual of Style (16th Edition):
Jiang, Yun. “Hallucinated Humans: Learning Latent Factors To Model 3D Environments.” 2015. Doctoral Dissertation, Cornell University. Accessed March 01, 2021.
http://hdl.handle.net/1813/41178.
MLA Handbook (7th Edition):
Jiang, Yun. “Hallucinated Humans: Learning Latent Factors To Model 3D Environments.” 2015. Web. 01 Mar 2021.
Vancouver:
Jiang Y. Hallucinated Humans: Learning Latent Factors To Model 3D Environments. [Internet] [Doctoral dissertation]. Cornell University; 2015. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1813/41178.
Council of Science Editors:
Jiang Y. Hallucinated Humans: Learning Latent Factors To Model 3D Environments. [Doctoral Dissertation]. Cornell University; 2015. Available from: http://hdl.handle.net/1813/41178
University of Edinburgh
24.
McConville, Dan.
Evidence of Encapsulated Learning Systems in an Alternating Serial Reaction Time Task.
Degree: 2011, University of Edinburgh
URL: http://hdl.handle.net/1842/6127
► A model of learning systems that has received wide acknowledgment is that promulgated by Squire (1992). The model describes explicit and implicit learning systems as…
(more)
▼ A model of
learning systems that has received wide acknowledgment is that
promulgated by Squire (1992). The model describes explicit and implicit
learning
systems as being encapsulated and operating in parallel. This paper aimed to
investigate if implicit procedural sequence
learning could be affected by explicit
learning processes in an alternating serial reaction time task (ASRTT). The ASRTT
involved visual-stimuli appearing in one of four spatial-locations placed horizontally
across a computer screen. The stimuli followed a four-unit repeating sequence which
alternated with random trials (1r2r4r3). This meant certain sets of three sequential
trials or triplets appeared at a high frequency (HFT) whilst others appeared at a low
frequency. Sequence
learning is represented by improved performance of response
time and accuracy when responding to the third trial of a HFT compared to a LFT.
This is called the triplet-type effect. The experiment also investigated if implicit
sequence
learning could be facilitated by observation alone. Results revealed that the
level of implicit sequence
learning occurred to the same extent, whether or not
subjects intentionally tried to learn the repeating sequence during the training phase.
The level of implicit sequence
learning was also equivalent between participants that
observed the visual stimuli in the training session and those that responded to it.
These findings indicate that implicit sequence
learning occurs independently of
explicit
learning processes with implicit sequence
learning also being facilitated by
observation alone. This study provides evidence that supports the theory of
encapsulated
learning systems.
Advisors/Committee Members: Morcom, Alexa.
Subjects/Keywords: Encapsulated learning systems; Procedural learning
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APA (6th Edition):
McConville, D. (2011). Evidence of Encapsulated Learning Systems in an Alternating Serial Reaction Time Task. (Thesis). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/6127
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
McConville, Dan. “Evidence of Encapsulated Learning Systems in an Alternating Serial Reaction Time Task.” 2011. Thesis, University of Edinburgh. Accessed March 01, 2021.
http://hdl.handle.net/1842/6127.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
McConville, Dan. “Evidence of Encapsulated Learning Systems in an Alternating Serial Reaction Time Task.” 2011. Web. 01 Mar 2021.
Vancouver:
McConville D. Evidence of Encapsulated Learning Systems in an Alternating Serial Reaction Time Task. [Internet] [Thesis]. University of Edinburgh; 2011. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1842/6127.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
McConville D. Evidence of Encapsulated Learning Systems in an Alternating Serial Reaction Time Task. [Thesis]. University of Edinburgh; 2011. Available from: http://hdl.handle.net/1842/6127
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of Johannesburg
25.
Alston, Graham Ernest.
A psycho-educational programme aimed at teachers to assist learners in accelerating learning.
Degree: 2009, University of Johannesburg
URL: http://hdl.handle.net/10210/2353
► D.Ed.
Teachers do not easily identify or use the thinking skills (support skills) present in themselves in their planning activities. The learners equally when they…
(more)
▼ D.Ed.
Teachers do not easily identify or use the thinking skills (support skills) present in themselves in their planning activities. The learners equally when they move from the Foundation phase to the Intermediate phase [from grade 3 to grade 4] do not reflect such support skills. Learners are thus unable to work quickly enough to acquire the new skills that are required in the next stage of their education. The problem of learning another language often encountered by learners when they come to formal school exacerbates the already challenging circumstances reflected in a country impacted by poverty. They have already acquired thinking skills in the process of learning their primary or home language. They often begin to learn this additional language during the foundation phase, and then in the intermediate phase they have to use the new language for the acquisition of knowledge and learning. In the schools in this study both the teachers and learners usually have to use the additional language as the language of learning and teaching (lolt). The main thrust of learning and teaching takes place in the intermediate phase where new subjects and learning areas are introduced and grounded. The research objectives of this study are to develop, implement and develop guideline for the evaluation of an educational programme that will assist Grade 4 teachers identify the schema or support skills that they use in planning for teaching and specifically language teaching. To enable the PDF created with pdfFactory Pro trial version www.pdffactory.com v teachers to act upon this information [data] in such a way that it will assist their learners with their learning needs during the first stage of the Intermediate Phase (grade 4) of their schooling, to promote faster learning. A long-term aspect of this intervention would be accelerated or assisted learning that will better equip the learner to more successfully move through all the phases of their education and eventually for life. The teachers would, as a result of the success of the programme, be further encouraged and motivated in their work. Further goals that arise out of the overall objectives of the study § Exploration and description of grade 4 teacher’s experience of scaffolding they use when planning their programmes with their learners. § Description of an approach to support the Grade 4 teacher and the teacher educator. § Description of guidelines to operationalise the support approach by the curriculum specialist. Qualitative strategies such as focus group interviews and naïve essays were engaged in and this resulted in the discovery that planning did not play an important part in the lives of the participant teachers nor for that matter were they using cognitive structures that led to planning. The lack of reflection on PDF created with pdfFactory Pro trial version www.pdffactory.com vi the meta-cognitive structures in their own lives led to the learners being undirected and thus impeding their learning. With this in mind a conceptual framework was developed…
Subjects/Keywords: Psychology of learning; Learning evaluation
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APA (6th Edition):
Alston, G. E. (2009). A psycho-educational programme aimed at teachers to assist learners in accelerating learning. (Thesis). University of Johannesburg. Retrieved from http://hdl.handle.net/10210/2353
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Alston, Graham Ernest. “A psycho-educational programme aimed at teachers to assist learners in accelerating learning.” 2009. Thesis, University of Johannesburg. Accessed March 01, 2021.
http://hdl.handle.net/10210/2353.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Alston, Graham Ernest. “A psycho-educational programme aimed at teachers to assist learners in accelerating learning.” 2009. Web. 01 Mar 2021.
Vancouver:
Alston GE. A psycho-educational programme aimed at teachers to assist learners in accelerating learning. [Internet] [Thesis]. University of Johannesburg; 2009. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/10210/2353.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Alston GE. A psycho-educational programme aimed at teachers to assist learners in accelerating learning. [Thesis]. University of Johannesburg; 2009. Available from: http://hdl.handle.net/10210/2353
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
University of KwaZulu-Natal
26.
Govender, Lishen.
Determination of quantum entanglement concurrence using multilayer perceptron neural networks.
Degree: 2017, University of KwaZulu-Natal
URL: http://hdl.handle.net/10413/15713
► Artificial Neural Networks, inspired by biological neural networks, have seen widespread implementations across all research areas in the past few years. This partly due to…
(more)
▼ Artificial Neural Networks, inspired by biological neural networks, have seen widespread
implementations across all research areas in the past few years. This partly due to recent
developments in the field and mostly due to the increased accessibility of hardware
and cloud computing capable of realising artificial neural network models. As
the implementation of neural networks and deep
learning in general becomes more
ubiquitous in everyday life, we seek to leverage this powerful tool to aid in furthering
research in quantum information science.
Concurrence is a measure of entanglement that quantifies the "amount" of entanglement
contained within both pure and mixed state entangled systems [1]. In this thesis,
artificial neural networks are used to determine models that predict concurrence, particularly,
models are trained on mixed state inputs and used for pure state prediction.
Conversely additional models are trained on pure state inputs and used for mixed state
prediction. An overview of the prediction performance is presented along with analysis
of the predictions.
Advisors/Committee Members: Petruccione, Francesco. (advisor), Sinayskiy, Llya. (advisor).
Subjects/Keywords: Deep learning.; Machine learning.
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Manager
APA (6th Edition):
Govender, L. (2017). Determination of quantum entanglement concurrence using multilayer perceptron neural networks. (Thesis). University of KwaZulu-Natal. Retrieved from http://hdl.handle.net/10413/15713
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Govender, Lishen. “Determination of quantum entanglement concurrence using multilayer perceptron neural networks.” 2017. Thesis, University of KwaZulu-Natal. Accessed March 01, 2021.
http://hdl.handle.net/10413/15713.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Govender, Lishen. “Determination of quantum entanglement concurrence using multilayer perceptron neural networks.” 2017. Web. 01 Mar 2021.
Vancouver:
Govender L. Determination of quantum entanglement concurrence using multilayer perceptron neural networks. [Internet] [Thesis]. University of KwaZulu-Natal; 2017. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/10413/15713.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Govender L. Determination of quantum entanglement concurrence using multilayer perceptron neural networks. [Thesis]. University of KwaZulu-Natal; 2017. Available from: http://hdl.handle.net/10413/15713
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Oregon State University
27.
Wilson, Aaron (Aaron Creighton).
Bayesian methods for knowledge transfer and policy search in reinforcement learning.
Degree: PhD, Computer Science, 2012, Oregon State University
URL: http://hdl.handle.net/1957/34550
► How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action…
(more)
▼ How can an agent generalize its knowledge to new circumstances? To learn
effectively an agent acting in a sequential decision problem must make intelligent action selection choices based on its available knowledge. This dissertation focuses on Bayesian methods of representing learned knowledge and develops novel algorithms that exploit the represented
knowledge when selecting actions.
Our first contribution introduces the multi-task Reinforcement
Learning setting in which an agent solves a sequence of tasks. An
agent equipped with knowledge of the relationship between tasks can
transfer knowledge between them. We propose the transfer of two
distinct types of knowledge: knowledge of domain models and knowledge
of policies. To represent the transferable knowledge, we propose
hierarchical Bayesian priors on domain models and policies
respectively. To transfer domain model knowledge, we introduce a new
algorithm for model-based Bayesian Reinforcement
Learning in the
multi-task setting which exploits the learned hierarchical Bayesian
model to improve exploration in related tasks. To transfer policy
knowledge, we introduce a new policy search algorithm that accepts a
policy prior as input and uses the prior to bias policy search. A
specific implementation of this algorithm is developed that accepts a
hierarchical policy prior. The algorithm learns the hierarchical
structure and reuses components of the structure in related tasks.
Our second contribution addresses the basic problem of generalizing knowledge gained from previously-executed policies. Bayesian
Optimization is a method of exploiting a prior model of an objective function to quickly identify the point maximizing the modeled objective.
Successful use of Bayesian Optimization in Reinforcement
Learning
requires a model relating policies and their performance. Given such a
model, Bayesian Optimization can be applied to search for an optimal
policy. Early work using Bayesian Optimization in the Reinforcement
Learning setting ignored the sequential nature of the underlying
decision problem. The work presented in this thesis explicitly
addresses this problem. We construct new Bayesian models that take
advantage of sequence information to better generalize knowledge
across policies. We empirically evaluate the value of this approach in
a variety of Reinforcement
Learning benchmark problems. Experiments
show that our method significantly reduces the amount of exploration
required to identify the optimal policy.
Our final contribution is a new framework for
learning parametric
policies from queries presented to an expert. In many domains it is
difficult to provide expert demonstrations of desired policies.
However, it may still be a simple matter for an expert to identify
good and bad performance. To take advantage of this limited expert
knowledge, our agent presents experts with pairs of demonstrations and
asks which of the demonstrations best represents a latent target
behavior. The goal is to use a small number of…
Advisors/Committee Members: Tadepalli, Prasad (advisor), Tom, Dietterich (committee member).
Subjects/Keywords: Machine Learning; Reinforcement learning
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APA (6th Edition):
Wilson, A. (. C. (2012). Bayesian methods for knowledge transfer and policy search in reinforcement learning. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/34550
Chicago Manual of Style (16th Edition):
Wilson, Aaron (Aaron Creighton). “Bayesian methods for knowledge transfer and policy search in reinforcement learning.” 2012. Doctoral Dissertation, Oregon State University. Accessed March 01, 2021.
http://hdl.handle.net/1957/34550.
MLA Handbook (7th Edition):
Wilson, Aaron (Aaron Creighton). “Bayesian methods for knowledge transfer and policy search in reinforcement learning.” 2012. Web. 01 Mar 2021.
Vancouver:
Wilson A(C. Bayesian methods for knowledge transfer and policy search in reinforcement learning. [Internet] [Doctoral dissertation]. Oregon State University; 2012. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1957/34550.
Council of Science Editors:
Wilson A(C. Bayesian methods for knowledge transfer and policy search in reinforcement learning. [Doctoral Dissertation]. Oregon State University; 2012. Available from: http://hdl.handle.net/1957/34550
Oregon State University
28.
Hooper, Samuel.
Spatial and Temporal Dynamics of Broad-scale Predictive Models : Influences of Scale.
Degree: MS, Geography, 2017, Oregon State University
URL: http://hdl.handle.net/1957/60148
► Developing accurate predictive distribution models requires adequately representing relevant spatial and temporal scales, as these scales are ultimately reflective of the relationships between distributions and…
(more)
▼ Developing accurate predictive distribution models requires adequately representing relevant spatial and temporal scales, as these scales are ultimately reflective of the relationships between distributions and influential environmental conditions. In this research, we considered both spatial and temporal scale and the influence each has on predicting broad-scale distributions of two disparate but related phenomena: land cover and bird distributions. Employing machine-
learning algorithms, we first developed land cover time series datasets covering all of California, Oregon, and Washington with a model that simultaneously reflects local-scale heterogeneity and broad-scale homogeneity. We then used these and other land cover time series datasets to investigate the effects of temporal resolution on species distribution models.
In the second chapter, we focused on the importance of accurately representing the spatial scale of relationships between predictors and a response variable for broad-scale predictive models. Using both a novel machine-
learning algorithm and a novel predictor dataset, we developed dense time series forest canopy cover (FCC) and impervious surface cover (ISC) datasets at a 30-meter spatial resolution for all of California, Oregon, and Washington. To develop both datasets, we employed a spatial ensemble modeling method using a population of locally defined and spatially overlapping decision trees, making it both appropriate at continental-scales and sensitive to local variation in predictor-response relationships. Our predictor variables were products of LandTrendr, a tool for developing time series images and derivatives from the Landsat archive. To develop the most accurate time series of FCC and ISC, we first tested two model parameters, sample size and estimator size. Using the best-performing configuration of each, we then compared our models with locally defined estimators to bagged decision trees, the most comparable model with globally defined estimators. Using the best-performing models and LandTrendr imagery, we developed yearly FCC and ISC maps, spanning 1990-2012. To test the temporal extensibility of our models, we compared our predicted 2011 maps to 2011 maps from the National Land Cover Database. We found that model performance for both FCC and ISC decreased with increasing estimator size and that models with locally defined estimators outperformed bagged decision trees. We also found that our models performed well when extending learned predictor-response relationships to predict 2011 FCC and ISC distributions. These results, in concert with several novel byproducts of the models that we developed, demonstrate that representing local-scale spatial relationships is critical to producing accurate broad-scale distribution models.
In the third chapter, we investigated the influence of temporal scale on an avian species distribution model (SDM) by comparing models developed with different temporal resolutions of land cover predictor data. We expressed temporal resolution as the time…
Advisors/Committee Members: Kennedy, Robert E. (advisor), Robinson, Douglas (committee member).
Subjects/Keywords: machine learning; Machine learning
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Hooper, S. (2017). Spatial and Temporal Dynamics of Broad-scale Predictive Models : Influences of Scale. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/60148
Chicago Manual of Style (16th Edition):
Hooper, Samuel. “Spatial and Temporal Dynamics of Broad-scale Predictive Models : Influences of Scale.” 2017. Masters Thesis, Oregon State University. Accessed March 01, 2021.
http://hdl.handle.net/1957/60148.
MLA Handbook (7th Edition):
Hooper, Samuel. “Spatial and Temporal Dynamics of Broad-scale Predictive Models : Influences of Scale.” 2017. Web. 01 Mar 2021.
Vancouver:
Hooper S. Spatial and Temporal Dynamics of Broad-scale Predictive Models : Influences of Scale. [Internet] [Masters thesis]. Oregon State University; 2017. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1957/60148.
Council of Science Editors:
Hooper S. Spatial and Temporal Dynamics of Broad-scale Predictive Models : Influences of Scale. [Masters Thesis]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/60148
Texas A&M University
29.
Kapale, Anurag.
An Automated Framework To Generate End-to-End Machine Learning Pipelines.
Degree: MS, Computer Science, 2019, Texas A&M University
URL: http://hdl.handle.net/1969.1/188756
► The recent developments in machine learning have shown its applicability in numerous real-world applications. However, building an optimal machine learning pipeline requires considerable knowledge and…
(more)
▼ The recent developments in machine
learning have shown its applicability in numerous real-world applications. However, building an optimal machine
learning pipeline requires considerable knowledge and experience in data science. To address this problem, many automated machine
learning (AutoML) frameworks have been proposed. However, most of the existing AutoML frameworks treat the pipeline generation as a black-box optimization problem. Thus, failing to incorporate basic heuristics and human intuition. Furthermore, most of these frameworks provide very basic or no feature engineering abilities. To tackle these challenges, in this thesis, we propose an automated framework to generate end-to-end machine
learning pipelines. By survey of 100s of Kaggle kernels and extensive experimentation, we finalized a set of heuristics which enhances the pipeline optimization problem. We also implemented a system to automate feature engineering, which could generate 100s of features to produce better representation of the data.
Additionally, the framework provides interpretations about why certain models and features were selected by the system. This would help the users to further improve the pipeline. Finally, our experimentation shows consistent performance across various datasets.
Advisors/Committee Members: Hu, Xia (advisor), Mortazavi, Bobak (committee member), Shen, Yang (committee member).
Subjects/Keywords: Machine Learning; Automated Machine Learning
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APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Kapale, A. (2019). An Automated Framework To Generate End-to-End Machine Learning Pipelines. (Masters Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/188756
Chicago Manual of Style (16th Edition):
Kapale, Anurag. “An Automated Framework To Generate End-to-End Machine Learning Pipelines.” 2019. Masters Thesis, Texas A&M University. Accessed March 01, 2021.
http://hdl.handle.net/1969.1/188756.
MLA Handbook (7th Edition):
Kapale, Anurag. “An Automated Framework To Generate End-to-End Machine Learning Pipelines.” 2019. Web. 01 Mar 2021.
Vancouver:
Kapale A. An Automated Framework To Generate End-to-End Machine Learning Pipelines. [Internet] [Masters thesis]. Texas A&M University; 2019. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/1969.1/188756.
Council of Science Editors:
Kapale A. An Automated Framework To Generate End-to-End Machine Learning Pipelines. [Masters Thesis]. Texas A&M University; 2019. Available from: http://hdl.handle.net/1969.1/188756
University of Adelaide
30.
Suksudaj, Nattira.
What factors influence learning of psychomotor skills by dental students?.
Degree: 2010, University of Adelaide
URL: http://hdl.handle.net/2440/63693
► Several key factors have been identified that relate to skill acquisition: a) ability, b) motivation, c) thinking processes and d) learning environments. Other health professions…
(more)
▼ Several key factors have been identified that relate to skill acquisition: a) ability, b) motivation, c) thinking processes and d)
learning environments. Other health professions have used
learning theories to inform study designs when investigating skill acquisition but this approach has not been adopted routinely in dentistry. Previous studies in dentistry have focused mainly on the predictive value of assessments used in dental admissions, eg ability tests, rather than trying to clarify how factors such as motivation and ability influence skill
learning. This dissertation explores the influence of the above key factors on dental performance and outlines theoretical-based implications for practice in operative technical courses.
To clarify how motivation, ability, thinking processes and
learning environments influence the acquisition of psychomotor skills in operative dentistry, two cohorts of dental students were studied from different years of the Bachelor of Dental Surgery program at The University of Adelaide. To determine the nature of the relationship between individual differences in ability, motivational determinants and performance on routine operative dentistry tasks, a cross-sectional study (Phase I) was conducted of third-year students. Phase I also investigated the use of motor
learning parameters by students during completion of a routine operative task.
The second phase of the study investigated individual differences in ability of a different cohort of students and was carried out during the second year. This was achieved by exploring the contribution of ability and motivation determinants to changes in motor performance throughout the operative technique course. The study also explored external factors that were related to performance, ie
learning experiences that students reported had influenced their skill
learning, as well as motor
learning parameters they used during the activities.
Both quantitative and qualitative approaches were used to explore the previously noted key factors using a range of instruments, eg psychometric tests, a motivation survey, a retrospective think-aloud technique and critical incident reports. Significant positive associations were found between cognitive, psychomotor and motivation scores and performance in operative dentistry. This relationship varied across different stages of
learning in the dental program. Students tended to focus on evaluating their outcome rather than evaluating their processes to achieve a task. Three themes related to
learning environments were derived from critical incident reports and follow-up interviews: roles of tutors in providing a positive
learning environment; perceptions about the quality of cavity preparations, ie “
learning from errors”; and roles of peers in self-assessment of outcomes.
This study has provided insights into individual differences in the
learning of psychomotor skills by dental students as a result of inherited factors, eg ability, as well as the roles of the
learning environment in enhancing
learning. This…
Advisors/Committee Members: Winning, Tracey Anne (advisor), Townsend, Grant Clement (advisor), Kaidonis, John Aristidis (advisor), Lekkas, Dimitra (advisor), School of Dentistry (school).
Subjects/Keywords: psychomotor skills; learning; learning experience
Record Details
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APA ·
Chicago ·
MLA ·
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CSE |
Export
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Manager
APA (6th Edition):
Suksudaj, N. (2010). What factors influence learning of psychomotor skills by dental students?. (Thesis). University of Adelaide. Retrieved from http://hdl.handle.net/2440/63693
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Suksudaj, Nattira. “What factors influence learning of psychomotor skills by dental students?.” 2010. Thesis, University of Adelaide. Accessed March 01, 2021.
http://hdl.handle.net/2440/63693.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Suksudaj, Nattira. “What factors influence learning of psychomotor skills by dental students?.” 2010. Web. 01 Mar 2021.
Vancouver:
Suksudaj N. What factors influence learning of psychomotor skills by dental students?. [Internet] [Thesis]. University of Adelaide; 2010. [cited 2021 Mar 01].
Available from: http://hdl.handle.net/2440/63693.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Suksudaj N. What factors influence learning of psychomotor skills by dental students?. [Thesis]. University of Adelaide; 2010. Available from: http://hdl.handle.net/2440/63693
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
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Open Access Theses and Dissertations