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You searched for subject:(machine learning models). Showing records 1 – 30 of 388 total matches.

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University of Cambridge

1. Bratières, Sébastien. Non-parametric Bayesian models for structured output prediction.

Degree: PhD, 2018, University of Cambridge

 Structured output prediction is a machine learning tasks in which an input object is not just assigned a single class, as in classification, but multiple,… (more)

Subjects/Keywords: machine learning; Bayesian models; probability

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bratières, S. (2018). Non-parametric Bayesian models for structured output prediction. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/274973 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744725

Chicago Manual of Style (16th Edition):

Bratières, Sébastien. “Non-parametric Bayesian models for structured output prediction.” 2018. Doctoral Dissertation, University of Cambridge. Accessed July 22, 2019. https://www.repository.cam.ac.uk/handle/1810/274973 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744725.

MLA Handbook (7th Edition):

Bratières, Sébastien. “Non-parametric Bayesian models for structured output prediction.” 2018. Web. 22 Jul 2019.

Vancouver:

Bratières S. Non-parametric Bayesian models for structured output prediction. [Internet] [Doctoral dissertation]. University of Cambridge; 2018. [cited 2019 Jul 22]. Available from: https://www.repository.cam.ac.uk/handle/1810/274973 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744725.

Council of Science Editors:

Bratières S. Non-parametric Bayesian models for structured output prediction. [Doctoral Dissertation]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/274973 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.744725


University of Cambridge

2. Bratières, Sébastien. Non-parametric Bayesian models for structured output prediction .

Degree: 2018, University of Cambridge

 Structured output prediction is a machine learning tasks in which an input object is not just assigned a single class, as in classification, but multiple,… (more)

Subjects/Keywords: machine learning; Bayesian models; probability

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Bratières, S. (2018). Non-parametric Bayesian models for structured output prediction . (Thesis). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/274973

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):

Bratières, Sébastien. “Non-parametric Bayesian models for structured output prediction .” 2018. Thesis, University of Cambridge. Accessed July 22, 2019. https://www.repository.cam.ac.uk/handle/1810/274973.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Bratières, Sébastien. “Non-parametric Bayesian models for structured output prediction .” 2018. Web. 22 Jul 2019.

Vancouver:

Bratières S. Non-parametric Bayesian models for structured output prediction . [Internet] [Thesis]. University of Cambridge; 2018. [cited 2019 Jul 22]. Available from: https://www.repository.cam.ac.uk/handle/1810/274973.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bratières S. Non-parametric Bayesian models for structured output prediction . [Thesis]. University of Cambridge; 2018. Available from: https://www.repository.cam.ac.uk/handle/1810/274973

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Georgia Tech

3. Shegheva, Snejana. A computational model for solving raven’s progressive matrices intelligence test.

Degree: MS, Computer Science, 2018, Georgia Tech

 Graphical models offer techniques for capturing the structure of many problems in real- world domains and provide means for representation, interpretation, and inference. The modeling… (more)

Subjects/Keywords: Probabilistic graphical models; Machine learning

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APA (6th Edition):

Shegheva, S. (2018). A computational model for solving raven’s progressive matrices intelligence test. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60191

Chicago Manual of Style (16th Edition):

Shegheva, Snejana. “A computational model for solving raven’s progressive matrices intelligence test.” 2018. Masters Thesis, Georgia Tech. Accessed July 22, 2019. http://hdl.handle.net/1853/60191.

MLA Handbook (7th Edition):

Shegheva, Snejana. “A computational model for solving raven’s progressive matrices intelligence test.” 2018. Web. 22 Jul 2019.

Vancouver:

Shegheva S. A computational model for solving raven’s progressive matrices intelligence test. [Internet] [Masters thesis]. Georgia Tech; 2018. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/1853/60191.

Council of Science Editors:

Shegheva S. A computational model for solving raven’s progressive matrices intelligence test. [Masters Thesis]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60191


University of Minnesota

4. Tao, Shaozhe. Scalable Optimization Methods for Machine Learning: Structures, Properties and Applications.

Degree: PhD, Industrial and Systems Engineering, 2018, University of Minnesota

 Many problems in machine learning can be formulated using optimization models with constraints that are well structured. Driven in part by such applications, the need… (more)

Subjects/Keywords: Machine Learning; Optimization; Structured Models

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APA (6th Edition):

Tao, S. (2018). Scalable Optimization Methods for Machine Learning: Structures, Properties and Applications. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/199006

Chicago Manual of Style (16th Edition):

Tao, Shaozhe. “Scalable Optimization Methods for Machine Learning: Structures, Properties and Applications.” 2018. Doctoral Dissertation, University of Minnesota. Accessed July 22, 2019. http://hdl.handle.net/11299/199006.

MLA Handbook (7th Edition):

Tao, Shaozhe. “Scalable Optimization Methods for Machine Learning: Structures, Properties and Applications.” 2018. Web. 22 Jul 2019.

Vancouver:

Tao S. Scalable Optimization Methods for Machine Learning: Structures, Properties and Applications. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/11299/199006.

Council of Science Editors:

Tao S. Scalable Optimization Methods for Machine Learning: Structures, Properties and Applications. [Doctoral Dissertation]. University of Minnesota; 2018. Available from: http://hdl.handle.net/11299/199006


University of New Mexico

5. Oyen, Diane. Interactive Exploration of Multitask Dependency Networks.

Degree: Department of Computer Science, 2013, University of New Mexico

 Scientists increasingly depend on machine learning algorithms to discover patterns in complex data. Two examples addressed in this dissertation are identifying how information sharing among… (more)

Subjects/Keywords: machine learning; probabilistic graphical models

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APA (6th Edition):

Oyen, D. (2013). Interactive Exploration of Multitask Dependency Networks. (Doctoral Dissertation). University of New Mexico. Retrieved from http://hdl.handle.net/1928/23359

Chicago Manual of Style (16th Edition):

Oyen, Diane. “Interactive Exploration of Multitask Dependency Networks.” 2013. Doctoral Dissertation, University of New Mexico. Accessed July 22, 2019. http://hdl.handle.net/1928/23359.

MLA Handbook (7th Edition):

Oyen, Diane. “Interactive Exploration of Multitask Dependency Networks.” 2013. Web. 22 Jul 2019.

Vancouver:

Oyen D. Interactive Exploration of Multitask Dependency Networks. [Internet] [Doctoral dissertation]. University of New Mexico; 2013. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/1928/23359.

Council of Science Editors:

Oyen D. Interactive Exploration of Multitask Dependency Networks. [Doctoral Dissertation]. University of New Mexico; 2013. Available from: http://hdl.handle.net/1928/23359


University of Texas – Austin

6. Zheng, Hanyue. KKBox subscription prediction : an application of machine learning methods.

Degree: Statistics, 2018, University of Texas – Austin

 This report used datasets from a Kaggle competition which aims to develop machine learning models to predict if users of a music app called KKBox… (more)

Subjects/Keywords: Machine learning; Classification; Machine learning models; Machine learning model development; Machine learning classification models; Machine learning model performance

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APA (6th Edition):

Zheng, H. (2018). KKBox subscription prediction : an application of machine learning methods. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/67638

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):

Zheng, Hanyue. “KKBox subscription prediction : an application of machine learning methods.” 2018. Thesis, University of Texas – Austin. Accessed July 22, 2019. http://hdl.handle.net/2152/67638.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zheng, Hanyue. “KKBox subscription prediction : an application of machine learning methods.” 2018. Web. 22 Jul 2019.

Vancouver:

Zheng H. KKBox subscription prediction : an application of machine learning methods. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/2152/67638.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zheng H. KKBox subscription prediction : an application of machine learning methods. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/67638

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Rutgers University

7. Leffler, Bethany R. Perception-based generalization in model-based reinforcement learning:.

Degree: PhD, Computer Science, 2009, Rutgers University

In recent years, the advances in robotics have allowed for robots to venture into places too dangerous for humans. Unfortunately, the terrain in which these… (more)

Subjects/Keywords: Reinforcement learning – Mathematical models; Machine learning

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APA (6th Edition):

Leffler, B. R. (2009). Perception-based generalization in model-based reinforcement learning:. (Doctoral Dissertation). Rutgers University. Retrieved from http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051041

Chicago Manual of Style (16th Edition):

Leffler, Bethany R. “Perception-based generalization in model-based reinforcement learning:.” 2009. Doctoral Dissertation, Rutgers University. Accessed July 22, 2019. http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051041.

MLA Handbook (7th Edition):

Leffler, Bethany R. “Perception-based generalization in model-based reinforcement learning:.” 2009. Web. 22 Jul 2019.

Vancouver:

Leffler BR. Perception-based generalization in model-based reinforcement learning:. [Internet] [Doctoral dissertation]. Rutgers University; 2009. [cited 2019 Jul 22]. Available from: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051041.

Council of Science Editors:

Leffler BR. Perception-based generalization in model-based reinforcement learning:. [Doctoral Dissertation]. Rutgers University; 2009. Available from: http://hdl.rutgers.edu/1782.2/rucore10001600001.ETD.000051041


Oregon State University

8. Hao, Guohua. Efficient training and feature induction in sequential supervised learning.

Degree: PhD, Computer Science, 2009, Oregon State University

 Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature… (more)

Subjects/Keywords: Machine Learning; Supervised learning (Machine learning)  – Mathematical models

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APA (6th Edition):

Hao, G. (2009). Efficient training and feature induction in sequential supervised learning. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/12548

Chicago Manual of Style (16th Edition):

Hao, Guohua. “Efficient training and feature induction in sequential supervised learning.” 2009. Doctoral Dissertation, Oregon State University. Accessed July 22, 2019. http://hdl.handle.net/1957/12548.

MLA Handbook (7th Edition):

Hao, Guohua. “Efficient training and feature induction in sequential supervised learning.” 2009. Web. 22 Jul 2019.

Vancouver:

Hao G. Efficient training and feature induction in sequential supervised learning. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/1957/12548.

Council of Science Editors:

Hao G. Efficient training and feature induction in sequential supervised learning. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/12548


ETH Zürich

9. Lucic, Mario. Computational and Statistical Tradeoffs via Data Summarization.

Degree: 2017, ETH Zürich

 The massive growth of modern datasets from different sources such as videos, social networks, and sensor data, coupled with limited resources in terms of time… (more)

Subjects/Keywords: Machine Learning; Coresets; Large-scale Machine Learning; Outlier Detection; Mixture Models

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APA (6th Edition):

Lucic, M. (2017). Computational and Statistical Tradeoffs via Data Summarization. (Doctoral Dissertation). ETH Zürich. Retrieved from http://hdl.handle.net/20.500.11850/220255

Chicago Manual of Style (16th Edition):

Lucic, Mario. “Computational and Statistical Tradeoffs via Data Summarization.” 2017. Doctoral Dissertation, ETH Zürich. Accessed July 22, 2019. http://hdl.handle.net/20.500.11850/220255.

MLA Handbook (7th Edition):

Lucic, Mario. “Computational and Statistical Tradeoffs via Data Summarization.” 2017. Web. 22 Jul 2019.

Vancouver:

Lucic M. Computational and Statistical Tradeoffs via Data Summarization. [Internet] [Doctoral dissertation]. ETH Zürich; 2017. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/20.500.11850/220255.

Council of Science Editors:

Lucic M. Computational and Statistical Tradeoffs via Data Summarization. [Doctoral Dissertation]. ETH Zürich; 2017. Available from: http://hdl.handle.net/20.500.11850/220255


University of Louisville

10. Chorowski, Jan. Learning understandable classifier models.

Degree: PhD, 2012, University of Louisville

  The topic of this dissertation is the automation of the process of extracting understandable patterns and rules from data. An unprecedented amount of data… (more)

Subjects/Keywords: Machine learning; Neural Network; white-box models

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APA (6th Edition):

Chorowski, J. (2012). Learning understandable classifier models. (Doctoral Dissertation). University of Louisville. Retrieved from 10.18297/etd/248 ; https://ir.library.louisville.edu/etd/248

Chicago Manual of Style (16th Edition):

Chorowski, Jan. “Learning understandable classifier models.” 2012. Doctoral Dissertation, University of Louisville. Accessed July 22, 2019. 10.18297/etd/248 ; https://ir.library.louisville.edu/etd/248.

MLA Handbook (7th Edition):

Chorowski, Jan. “Learning understandable classifier models.” 2012. Web. 22 Jul 2019.

Vancouver:

Chorowski J. Learning understandable classifier models. [Internet] [Doctoral dissertation]. University of Louisville; 2012. [cited 2019 Jul 22]. Available from: 10.18297/etd/248 ; https://ir.library.louisville.edu/etd/248.

Council of Science Editors:

Chorowski J. Learning understandable classifier models. [Doctoral Dissertation]. University of Louisville; 2012. Available from: 10.18297/etd/248 ; https://ir.library.louisville.edu/etd/248


Montana State University

11. Tosun, Hasari. Efficient machine learning using partitioned restricted Boltzmann machines.

Degree: College of Engineering, 2016, Montana State University

 Restricted Boltzmann Machines (RBM) are energy-based models that are used as generative learning models as well as crucial components of Deep Belief Networks (DBN). The… (more)

Subjects/Keywords: Machine learning.; Mathematical models.; Stochastic processes.

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APA (6th Edition):

Tosun, H. (2016). Efficient machine learning using partitioned restricted Boltzmann machines. (Thesis). Montana State University. Retrieved from https://scholarworks.montana.edu/xmlui/handle/1/14329

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):

Tosun, Hasari. “Efficient machine learning using partitioned restricted Boltzmann machines.” 2016. Thesis, Montana State University. Accessed July 22, 2019. https://scholarworks.montana.edu/xmlui/handle/1/14329.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Tosun, Hasari. “Efficient machine learning using partitioned restricted Boltzmann machines.” 2016. Web. 22 Jul 2019.

Vancouver:

Tosun H. Efficient machine learning using partitioned restricted Boltzmann machines. [Internet] [Thesis]. Montana State University; 2016. [cited 2019 Jul 22]. Available from: https://scholarworks.montana.edu/xmlui/handle/1/14329.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tosun H. Efficient machine learning using partitioned restricted Boltzmann machines. [Thesis]. Montana State University; 2016. Available from: https://scholarworks.montana.edu/xmlui/handle/1/14329

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Hong Kong University of Science and Technology

12. Kang, Sida ECE. Surface defects detection based on unsupervised learning.

Degree: 2017, Hong Kong University of Science and Technology

 While in surface defects detection applications, detectors based on supervised learning can achieve high accuracy, their requirement of plentiful well-balanced labeled training set and vulnerability… (more)

Subjects/Keywords: Surfaces (Technology); Defects; Mathematical models; Machine learning

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APA (6th Edition):

Kang, S. E. (2017). Surface defects detection based on unsupervised learning. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-991012554662403412 ; http://repository.ust.hk/ir/bitstream/1783.1-91068/1/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):

Kang, Sida ECE. “Surface defects detection based on unsupervised learning.” 2017. Thesis, Hong Kong University of Science and Technology. Accessed July 22, 2019. https://doi.org/10.14711/thesis-991012554662403412 ; http://repository.ust.hk/ir/bitstream/1783.1-91068/1/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):

Kang, Sida ECE. “Surface defects detection based on unsupervised learning.” 2017. Web. 22 Jul 2019.

Vancouver:

Kang SE. Surface defects detection based on unsupervised learning. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2017. [cited 2019 Jul 22]. Available from: https://doi.org/10.14711/thesis-991012554662403412 ; http://repository.ust.hk/ir/bitstream/1783.1-91068/1/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:

Kang SE. Surface defects detection based on unsupervised learning. [Thesis]. Hong Kong University of Science and Technology; 2017. Available from: https://doi.org/10.14711/thesis-991012554662403412 ; http://repository.ust.hk/ir/bitstream/1783.1-91068/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Georgia Tech

13. Hrolenok, Brian Paul. Constructing and evaluating executable models of collective behavior.

Degree: PhD, Computer Science, 2018, Georgia Tech

 Multiagent simulation (MAS) can be a valuable tool for biologists and ethologists studying collective animal behavior. However, constructing models for simulation is often a time-consuming… (more)

Subjects/Keywords: Executable models; Machine learning; Multiagent systems

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APA (6th Edition):

Hrolenok, B. P. (2018). Constructing and evaluating executable models of collective behavior. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60751

Chicago Manual of Style (16th Edition):

Hrolenok, Brian Paul. “Constructing and evaluating executable models of collective behavior.” 2018. Doctoral Dissertation, Georgia Tech. Accessed July 22, 2019. http://hdl.handle.net/1853/60751.

MLA Handbook (7th Edition):

Hrolenok, Brian Paul. “Constructing and evaluating executable models of collective behavior.” 2018. Web. 22 Jul 2019.

Vancouver:

Hrolenok BP. Constructing and evaluating executable models of collective behavior. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/1853/60751.

Council of Science Editors:

Hrolenok BP. Constructing and evaluating executable models of collective behavior. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60751


Linköping University

14. Garg, Ankita. Forecasting exchage rates using machine learning models with time-varying volatility.

Degree: Statistics, 2012, Linköping University

  This thesis is focused on investigating the predictability of exchange rate returns on monthly and daily frequency using models that have been mostly developed… (more)

Subjects/Keywords: Forecasting; exchange rates; machine learning models

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APA (6th Edition):

Garg, A. (2012). Forecasting exchage rates using machine learning models with time-varying volatility. (Thesis). Linköping University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79053

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):

Garg, Ankita. “Forecasting exchage rates using machine learning models with time-varying volatility.” 2012. Thesis, Linköping University. Accessed July 22, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79053.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Garg, Ankita. “Forecasting exchage rates using machine learning models with time-varying volatility.” 2012. Web. 22 Jul 2019.

Vancouver:

Garg A. Forecasting exchage rates using machine learning models with time-varying volatility. [Internet] [Thesis]. Linköping University; 2012. [cited 2019 Jul 22]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79053.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Garg A. Forecasting exchage rates using machine learning models with time-varying volatility. [Thesis]. Linköping University; 2012. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-79053

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Toronto

15. Russell, Travis. An MDP-based Coupon Issuing System.

Degree: 2015, University of Toronto

We present a system based on the work of Shani et al. [An MDP-based recommender system," Journal of Machine Learning Research, vol. 6, pp. 1265-1295,… (more)

Subjects/Keywords: machine learning; probabilistic models; recommender systems; 0405

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APA (6th Edition):

Russell, T. (2015). An MDP-based Coupon Issuing System. (Masters Thesis). University of Toronto. Retrieved from http://hdl.handle.net/1807/70564

Chicago Manual of Style (16th Edition):

Russell, Travis. “An MDP-based Coupon Issuing System.” 2015. Masters Thesis, University of Toronto. Accessed July 22, 2019. http://hdl.handle.net/1807/70564.

MLA Handbook (7th Edition):

Russell, Travis. “An MDP-based Coupon Issuing System.” 2015. Web. 22 Jul 2019.

Vancouver:

Russell T. An MDP-based Coupon Issuing System. [Internet] [Masters thesis]. University of Toronto; 2015. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/1807/70564.

Council of Science Editors:

Russell T. An MDP-based Coupon Issuing System. [Masters Thesis]. University of Toronto; 2015. Available from: http://hdl.handle.net/1807/70564


Australian National University

16. Zhang, Xinhua. Graphical Models: Modeling, Optimization, and Hilbert Space Embedding .

Degree: 2010, Australian National University

 Over the past two decades graphical models have been widely used as a powerful tool for compactly representing distributions. On the other hand, kernel methods… (more)

Subjects/Keywords: Machine Learning; Graphical Models; Kernel Methods; Optimization

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APA (6th Edition):

Zhang, X. (2010). Graphical Models: Modeling, Optimization, and Hilbert Space Embedding . (Thesis). Australian National University. Retrieved from http://hdl.handle.net/1885/49340

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):

Zhang, Xinhua. “Graphical Models: Modeling, Optimization, and Hilbert Space Embedding .” 2010. Thesis, Australian National University. Accessed July 22, 2019. http://hdl.handle.net/1885/49340.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Zhang, Xinhua. “Graphical Models: Modeling, Optimization, and Hilbert Space Embedding .” 2010. Web. 22 Jul 2019.

Vancouver:

Zhang X. Graphical Models: Modeling, Optimization, and Hilbert Space Embedding . [Internet] [Thesis]. Australian National University; 2010. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/1885/49340.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Zhang X. Graphical Models: Modeling, Optimization, and Hilbert Space Embedding . [Thesis]. Australian National University; 2010. Available from: http://hdl.handle.net/1885/49340

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Michigan State University

17. Hu, Xianfeng. Machine learning method for authorship attribution.

Degree: 2015, Michigan State University

Thesis Ph. D. Michigan State University. Applied Mathematics 2015

MACHINE LEARNING METHOD FOR AUTHORSHIP ATTRIBUTIONBy Xianfeng HuBroadly speaking, the authorship identification or authorship attribution problem… (more)

Subjects/Keywords: Machine learning – Case studies; Machine learning – Mathematical models; Authorship – Research – Mathematical models; Authorship, Disputed – Research – Mathematical models; Mathematics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hu, X. (2015). Machine learning method for authorship attribution. (Thesis). Michigan State University. Retrieved from http://etd.lib.msu.edu/islandora/object/etd:3660

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):

Hu, Xianfeng. “Machine learning method for authorship attribution.” 2015. Thesis, Michigan State University. Accessed July 22, 2019. http://etd.lib.msu.edu/islandora/object/etd:3660.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Hu, Xianfeng. “Machine learning method for authorship attribution.” 2015. Web. 22 Jul 2019.

Vancouver:

Hu X. Machine learning method for authorship attribution. [Internet] [Thesis]. Michigan State University; 2015. [cited 2019 Jul 22]. Available from: http://etd.lib.msu.edu/islandora/object/etd:3660.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Hu X. Machine learning method for authorship attribution. [Thesis]. Michigan State University; 2015. Available from: http://etd.lib.msu.edu/islandora/object/etd:3660

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Illinois – Chicago

18. Nazarian, Ebrahim. Machine Learning, Probabilistic and Mathematical Models for Damage Recognition in Structural Systems.

Degree: 2017, University of Illinois – Chicago

 An increasing percentage of building and bridge structures across United States are exceeding their design life. Ensuring the structural integrity of such structures demands health… (more)

Subjects/Keywords: Machine learning; Mathematical Models; Statistical Models; Structural Health Monitoring

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Nazarian, E. (2017). Machine Learning, Probabilistic and Mathematical Models for Damage Recognition in Structural Systems. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/21922

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):

Nazarian, Ebrahim. “Machine Learning, Probabilistic and Mathematical Models for Damage Recognition in Structural Systems.” 2017. Thesis, University of Illinois – Chicago. Accessed July 22, 2019. http://hdl.handle.net/10027/21922.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Nazarian, Ebrahim. “Machine Learning, Probabilistic and Mathematical Models for Damage Recognition in Structural Systems.” 2017. Web. 22 Jul 2019.

Vancouver:

Nazarian E. Machine Learning, Probabilistic and Mathematical Models for Damage Recognition in Structural Systems. [Internet] [Thesis]. University of Illinois – Chicago; 2017. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/10027/21922.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Nazarian E. Machine Learning, Probabilistic and Mathematical Models for Damage Recognition in Structural Systems. [Thesis]. University of Illinois – Chicago; 2017. Available from: http://hdl.handle.net/10027/21922

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Hong Kong University of Science and Technology

19. Ruan, Yang. Smooth and locally linear semi-supervised metric learning.

Degree: 2009, Hong Kong University of Science and Technology

 Many algorithms in pattern recognition and machine learning make use of some distance function explicitly or implicitly to characterize the relationships between data instances. Choosing… (more)

Subjects/Keywords: Machine learning  – Mathematical models; Pattern recognition systems  – Mathematical models

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Ruan, Y. (2009). Smooth and locally linear semi-supervised metric learning. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-b1054301 ; http://repository.ust.hk/ir/bitstream/1783.1-6069/1/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):

Ruan, Yang. “Smooth and locally linear semi-supervised metric learning.” 2009. Thesis, Hong Kong University of Science and Technology. Accessed July 22, 2019. https://doi.org/10.14711/thesis-b1054301 ; http://repository.ust.hk/ir/bitstream/1783.1-6069/1/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):

Ruan, Yang. “Smooth and locally linear semi-supervised metric learning.” 2009. Web. 22 Jul 2019.

Vancouver:

Ruan Y. Smooth and locally linear semi-supervised metric learning. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2009. [cited 2019 Jul 22]. Available from: https://doi.org/10.14711/thesis-b1054301 ; http://repository.ust.hk/ir/bitstream/1783.1-6069/1/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:

Ruan Y. Smooth and locally linear semi-supervised metric learning. [Thesis]. Hong Kong University of Science and Technology; 2009. Available from: https://doi.org/10.14711/thesis-b1054301 ; http://repository.ust.hk/ir/bitstream/1783.1-6069/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Hong Kong University of Science and Technology

20. Bi, Wei. Multilabel classification with label structures.

Degree: 2015, Hong Kong University of Science and Technology

 Many real-world applications involve multilabel classification, in which multiple labels can be associated with each sample. In many multilabel applications, structures exist among labels. A… (more)

Subjects/Keywords: Machine learning; Mathematical models; Classification; Multilevel models (Statistics)

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APA (6th Edition):

Bi, W. (2015). Multilabel classification with label structures. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-b1514779 ; http://repository.ust.hk/ir/bitstream/1783.1-78846/1/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):

Bi, Wei. “Multilabel classification with label structures.” 2015. Thesis, Hong Kong University of Science and Technology. Accessed July 22, 2019. https://doi.org/10.14711/thesis-b1514779 ; http://repository.ust.hk/ir/bitstream/1783.1-78846/1/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):

Bi, Wei. “Multilabel classification with label structures.” 2015. Web. 22 Jul 2019.

Vancouver:

Bi W. Multilabel classification with label structures. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2015. [cited 2019 Jul 22]. Available from: https://doi.org/10.14711/thesis-b1514779 ; http://repository.ust.hk/ir/bitstream/1783.1-78846/1/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:

Bi W. Multilabel classification with label structures. [Thesis]. Hong Kong University of Science and Technology; 2015. Available from: https://doi.org/10.14711/thesis-b1514779 ; http://repository.ust.hk/ir/bitstream/1783.1-78846/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Illinois – Urbana-Champaign

21. Garshasebi, Behnoush. Machine-learning-based vehicle delay prediction at signalized intersections.

Degree: MS, Civil Engineering, 2018, University of Illinois – Urbana-Champaign

 Delay is one of the critical elements of signalized intersections performance measures. Field delay calculations are usually time-consuming and inefficient and dominantly rely on manual… (more)

Subjects/Keywords: vehicle delay; machine learning; prediction models; data-driven models; delay prediction.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Garshasebi, B. (2018). Machine-learning-based vehicle delay prediction at signalized intersections. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/102430

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):

Garshasebi, Behnoush. “Machine-learning-based vehicle delay prediction at signalized intersections.” 2018. Thesis, University of Illinois – Urbana-Champaign. Accessed July 22, 2019. http://hdl.handle.net/2142/102430.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Garshasebi, Behnoush. “Machine-learning-based vehicle delay prediction at signalized intersections.” 2018. Web. 22 Jul 2019.

Vancouver:

Garshasebi B. Machine-learning-based vehicle delay prediction at signalized intersections. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2018. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/2142/102430.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Garshasebi B. Machine-learning-based vehicle delay prediction at signalized intersections. [Thesis]. University of Illinois – Urbana-Champaign; 2018. Available from: http://hdl.handle.net/2142/102430

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Oregon State University

22. Wynkoop, Michael S. Learning MDP action models via discrete mixture trees.

Degree: MS, Computer Science, 2008, Oregon State University

 This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the… (more)

Subjects/Keywords: Dynamic Bayesian Network; Reinforcement learning (Machine learning)  – Mathematical models

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APA (6th Edition):

Wynkoop, M. S. (2008). Learning MDP action models via discrete mixture trees. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/9096

Chicago Manual of Style (16th Edition):

Wynkoop, Michael S. “Learning MDP action models via discrete mixture trees.” 2008. Masters Thesis, Oregon State University. Accessed July 22, 2019. http://hdl.handle.net/1957/9096.

MLA Handbook (7th Edition):

Wynkoop, Michael S. “Learning MDP action models via discrete mixture trees.” 2008. Web. 22 Jul 2019.

Vancouver:

Wynkoop MS. Learning MDP action models via discrete mixture trees. [Internet] [Masters thesis]. Oregon State University; 2008. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/1957/9096.

Council of Science Editors:

Wynkoop MS. Learning MDP action models via discrete mixture trees. [Masters Thesis]. Oregon State University; 2008. Available from: http://hdl.handle.net/1957/9096


McMaster University

23. Blostein, Martin. An Efficient Implementation of a Robust Clustering Algorithm.

Degree: MSc, 2016, McMaster University

Clustering and classification are fundamental problems in statistical and machine learning, with a broad range of applications. A common approach is the Gaussian mixture model,… (more)

Subjects/Keywords: clustering; classification; statistical learning; machine learning; robust; computational statistics; mixture models

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APA (6th Edition):

Blostein, M. (2016). An Efficient Implementation of a Robust Clustering Algorithm. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/20598

Chicago Manual of Style (16th Edition):

Blostein, Martin. “An Efficient Implementation of a Robust Clustering Algorithm.” 2016. Masters Thesis, McMaster University. Accessed July 22, 2019. http://hdl.handle.net/11375/20598.

MLA Handbook (7th Edition):

Blostein, Martin. “An Efficient Implementation of a Robust Clustering Algorithm.” 2016. Web. 22 Jul 2019.

Vancouver:

Blostein M. An Efficient Implementation of a Robust Clustering Algorithm. [Internet] [Masters thesis]. McMaster University; 2016. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/11375/20598.

Council of Science Editors:

Blostein M. An Efficient Implementation of a Robust Clustering Algorithm. [Masters Thesis]. McMaster University; 2016. Available from: http://hdl.handle.net/11375/20598


University of Edinburgh

24. Heess, Nicolas Manfred Otto. Learning generative models of mid-level structure in natural images.

Degree: PhD, 2012, University of Edinburgh

 Natural images arise from complicated processes involving many factors of variation. They reflect the wealth of shapes and appearances of objects in our three-dimensional world,… (more)

Subjects/Keywords: 006.3; machine learning; unsupervised learning; generative models; computer vision

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Heess, N. M. O. (2012). Learning generative models of mid-level structure in natural images. (Doctoral Dissertation). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/5866

Chicago Manual of Style (16th Edition):

Heess, Nicolas Manfred Otto. “Learning generative models of mid-level structure in natural images.” 2012. Doctoral Dissertation, University of Edinburgh. Accessed July 22, 2019. http://hdl.handle.net/1842/5866.

MLA Handbook (7th Edition):

Heess, Nicolas Manfred Otto. “Learning generative models of mid-level structure in natural images.” 2012. Web. 22 Jul 2019.

Vancouver:

Heess NMO. Learning generative models of mid-level structure in natural images. [Internet] [Doctoral dissertation]. University of Edinburgh; 2012. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/1842/5866.

Council of Science Editors:

Heess NMO. Learning generative models of mid-level structure in natural images. [Doctoral Dissertation]. University of Edinburgh; 2012. Available from: http://hdl.handle.net/1842/5866


Université de Montréal

25. Breuleux, Olivier. Échantillonnage dynamique de champs markoviens .

Degree: 2010, Université de Montréal

 L'un des modèles d'apprentissage non-supervisé générant le plus de recherche active est la machine de Boltzmann  – en particulier la machine de Boltzmann restreinte, ou… (more)

Subjects/Keywords: Apprentissage machine; Champs markoviens; Machine de Boltzmann; MCMC; Modèles probabilistes; Machine learning; Markov random fields; Boltzmann machine; MCMC; Probabilistic models

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Breuleux, O. (2010). Échantillonnage dynamique de champs markoviens . (Thesis). Université de Montréal. Retrieved from http://hdl.handle.net/1866/4316

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):

Breuleux, Olivier. “Échantillonnage dynamique de champs markoviens .” 2010. Thesis, Université de Montréal. Accessed July 22, 2019. http://hdl.handle.net/1866/4316.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Breuleux, Olivier. “Échantillonnage dynamique de champs markoviens .” 2010. Web. 22 Jul 2019.

Vancouver:

Breuleux O. Échantillonnage dynamique de champs markoviens . [Internet] [Thesis]. Université de Montréal; 2010. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/1866/4316.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Breuleux O. Échantillonnage dynamique de champs markoviens . [Thesis]. Université de Montréal; 2010. Available from: http://hdl.handle.net/1866/4316

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

26. Rüping, Stefan. Learning interpretable models.

Degree: 2006, Technische Universität Dortmund

 Interpretability is an important, yet often neglected criterion when applying machine learning algorithms to real-world tasks. An understandable model enables the user to gain more… (more)

Subjects/Keywords: Classification; Data mining; Interpretability; Local models; Local patterns; Machine learning; 004

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APA (6th Edition):

Rüping, S. (2006). Learning interpretable models. (Thesis). Technische Universität Dortmund. Retrieved from http://hdl.handle.net/2003/23008

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):

Rüping, Stefan. “Learning interpretable models.” 2006. Thesis, Technische Universität Dortmund. Accessed July 22, 2019. http://hdl.handle.net/2003/23008.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Rüping, Stefan. “Learning interpretable models.” 2006. Web. 22 Jul 2019.

Vancouver:

Rüping S. Learning interpretable models. [Internet] [Thesis]. Technische Universität Dortmund; 2006. [cited 2019 Jul 22]. Available from: http://hdl.handle.net/2003/23008.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Rüping S. Learning interpretable models. [Thesis]. Technische Universität Dortmund; 2006. Available from: http://hdl.handle.net/2003/23008

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Brunel University

27. Ceccon, Stefano. Extending Bayesian network models for mining and classification of glaucoma.

Degree: PhD, 2013, Brunel University

 Glaucoma is a degenerative disease that damages the nerve fiber layer in the retina of the eye. Its mechanisms are not fully known and there… (more)

Subjects/Keywords: Data analysis; Data mining; Machine learning; State space models; Artifical intelligence

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APA (6th Edition):

Ceccon, S. (2013). Extending Bayesian network models for mining and classification of glaucoma. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/8051 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.589987

Chicago Manual of Style (16th Edition):

Ceccon, Stefano. “Extending Bayesian network models for mining and classification of glaucoma.” 2013. Doctoral Dissertation, Brunel University. Accessed July 22, 2019. http://bura.brunel.ac.uk/handle/2438/8051 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.589987.

MLA Handbook (7th Edition):

Ceccon, Stefano. “Extending Bayesian network models for mining and classification of glaucoma.” 2013. Web. 22 Jul 2019.

Vancouver:

Ceccon S. Extending Bayesian network models for mining and classification of glaucoma. [Internet] [Doctoral dissertation]. Brunel University; 2013. [cited 2019 Jul 22]. Available from: http://bura.brunel.ac.uk/handle/2438/8051 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.589987.

Council of Science Editors:

Ceccon S. Extending Bayesian network models for mining and classification of glaucoma. [Doctoral Dissertation]. Brunel University; 2013. Available from: http://bura.brunel.ac.uk/handle/2438/8051 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.589987


Addis Ababa University

28. TADESSE, BEYENE. MINING VITAL STATISTICS DATA: THE CASE OF BUTAJIRA RURAL HEALTH PROGRAM .

Degree: 2012, Addis Ababa University

 Data mining is a relatively new field whose major objective is to extract knowledge hidden in large amounts of data. Vital statistics data offer a… (more)

Subjects/Keywords: vital statistics data; Machine Learning; data mining; predictive models; classification; Weka.

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APA (6th Edition):

TADESSE, B. (2012). MINING VITAL STATISTICS DATA: THE CASE OF BUTAJIRA RURAL HEALTH PROGRAM . (Thesis). Addis Ababa University. Retrieved from http://etd.aau.edu.et/dspace/handle/123456789/2761

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):

TADESSE, BEYENE. “MINING VITAL STATISTICS DATA: THE CASE OF BUTAJIRA RURAL HEALTH PROGRAM .” 2012. Thesis, Addis Ababa University. Accessed July 22, 2019. http://etd.aau.edu.et/dspace/handle/123456789/2761.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

TADESSE, BEYENE. “MINING VITAL STATISTICS DATA: THE CASE OF BUTAJIRA RURAL HEALTH PROGRAM .” 2012. Web. 22 Jul 2019.

Vancouver:

TADESSE B. MINING VITAL STATISTICS DATA: THE CASE OF BUTAJIRA RURAL HEALTH PROGRAM . [Internet] [Thesis]. Addis Ababa University; 2012. [cited 2019 Jul 22]. Available from: http://etd.aau.edu.et/dspace/handle/123456789/2761.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

TADESSE B. MINING VITAL STATISTICS DATA: THE CASE OF BUTAJIRA RURAL HEALTH PROGRAM . [Thesis]. Addis Ababa University; 2012. Available from: http://etd.aau.edu.et/dspace/handle/123456789/2761

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Universiteit Utrecht

29. Wijk, D.C. van. The Descriptive Power of Chords: Music or Noise?.

Degree: 2016, Universiteit Utrecht

 In the field of music information retrieval (MIR), several features can be extracted from audio, which can then be used for tasks such as query-by-humming… (more)

Subjects/Keywords: Chord extraction; MIR; Machine learning; Classification; Segmentation; Regression; Language Models

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wijk, D. C. v. (2016). The Descriptive Power of Chords: Music or Noise?. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/330958

Chicago Manual of Style (16th Edition):

Wijk, D C van. “The Descriptive Power of Chords: Music or Noise?.” 2016. Masters Thesis, Universiteit Utrecht. Accessed July 22, 2019. http://dspace.library.uu.nl:8080/handle/1874/330958.

MLA Handbook (7th Edition):

Wijk, D C van. “The Descriptive Power of Chords: Music or Noise?.” 2016. Web. 22 Jul 2019.

Vancouver:

Wijk DCv. The Descriptive Power of Chords: Music or Noise?. [Internet] [Masters thesis]. Universiteit Utrecht; 2016. [cited 2019 Jul 22]. Available from: http://dspace.library.uu.nl:8080/handle/1874/330958.

Council of Science Editors:

Wijk DCv. The Descriptive Power of Chords: Music or Noise?. [Masters Thesis]. Universiteit Utrecht; 2016. Available from: http://dspace.library.uu.nl:8080/handle/1874/330958


Hong Kong University of Science and Technology

30. Xie, Ruiming. Transfer learning for one-class recommendation based on matrix factorization.

Degree: 2015, Hong Kong University of Science and Technology

 The One Class Recommender System aims at predicting users future behaviors according to their historical actions. In these problems, the training data usually only contains… (more)

Subjects/Keywords: Electronic data processing; Mathematical models; Machine learning; Database management

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Xie, R. (2015). Transfer learning for one-class recommendation based on matrix factorization. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-b1450557 ; http://repository.ust.hk/ir/bitstream/1783.1-74339/1/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):

Xie, Ruiming. “Transfer learning for one-class recommendation based on matrix factorization.” 2015. Thesis, Hong Kong University of Science and Technology. Accessed July 22, 2019. https://doi.org/10.14711/thesis-b1450557 ; http://repository.ust.hk/ir/bitstream/1783.1-74339/1/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):

Xie, Ruiming. “Transfer learning for one-class recommendation based on matrix factorization.” 2015. Web. 22 Jul 2019.

Vancouver:

Xie R. Transfer learning for one-class recommendation based on matrix factorization. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2015. [cited 2019 Jul 22]. Available from: https://doi.org/10.14711/thesis-b1450557 ; http://repository.ust.hk/ir/bitstream/1783.1-74339/1/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:

Xie R. Transfer learning for one-class recommendation based on matrix factorization. [Thesis]. Hong Kong University of Science and Technology; 2015. Available from: https://doi.org/10.14711/thesis-b1450557 ; http://repository.ust.hk/ir/bitstream/1783.1-74339/1/th_redirect.html

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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