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You searched for +publisher:"Oregon State University" +contributor:("Dietterich, Thomas"). Showing records 1 – 30 of 60 total matches.

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Oregon State University

1. Proper, Scott. Scaling multiagent reinforcement learning.

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

 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)

Subjects/Keywords: Reinforcement learning; Reinforcement learning

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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 April 24, 2019. http://hdl.handle.net/1957/13662.

MLA Handbook (7th Edition):

Proper, Scott. “Scaling multiagent reinforcement learning.” 2009. Web. 24 Apr 2019.

Vancouver:

Proper S. Scaling multiagent reinforcement learning. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2019 Apr 24]. 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

2. 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 April 24, 2019. http://hdl.handle.net/1957/9096.

MLA Handbook (7th Edition):

Wynkoop, Michael S. “Learning MDP action models via discrete mixture trees.” 2008. Web. 24 Apr 2019.

Vancouver:

Wynkoop MS. Learning MDP action models via discrete mixture trees. [Internet] [Masters thesis]. Oregon State University; 2008. [cited 2019 Apr 24]. 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


Oregon State University

3. Hao, Guohua. Revisiting output coding for sequential supervised learning.

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

 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)

Subjects/Keywords: ECOC; Supervised learning (Machine learning)

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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 April 24, 2019. http://hdl.handle.net/1957/10897.

MLA Handbook (7th Edition):

Hao, Guohua. “Revisiting output coding for sequential supervised learning.” 2009. Web. 24 Apr 2019.

Vancouver:

Hao G. Revisiting output coding for sequential supervised learning. [Internet] [Masters thesis]. Oregon State University; 2009. [cited 2019 Apr 24]. 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

4. Zhang, Wei. Image features and learning algorithms for biological, generic and social object recognition.

Degree: PhD, Electrical and Computer Engineering, 2009, Oregon State University

 Automated recognition of object categories in images is a critical step for many real-world computer vision applications. Interest region detectors and region descriptors have been… (more)

Subjects/Keywords: object recognition; Optical pattern recognition  – Mathematical models

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APA (6th Edition):

Zhang, W. (2009). Image features and learning algorithms for biological, generic and social object recognition. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/11178

Chicago Manual of Style (16th Edition):

Zhang, Wei. “Image features and learning algorithms for biological, generic and social object recognition.” 2009. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/11178.

MLA Handbook (7th Edition):

Zhang, Wei. “Image features and learning algorithms for biological, generic and social object recognition.” 2009. Web. 24 Apr 2019.

Vancouver:

Zhang W. Image features and learning algorithms for biological, generic and social object recognition. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/11178.

Council of Science Editors:

Zhang W. Image features and learning algorithms for biological, generic and social object recognition. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/11178


Oregon State University

5. Bao, Xinlong. Applying machine learning for prediction, recommendation, and integration.

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

 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)

Subjects/Keywords: machine learning; Machine learning

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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 April 24, 2019. http://hdl.handle.net/1957/12549.

MLA Handbook (7th Edition):

Bao, Xinlong. “Applying machine learning for prediction, recommendation, and integration.” 2009. Web. 24 Apr 2019.

Vancouver:

Bao X. Applying machine learning for prediction, recommendation, and integration. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2019 Apr 24]. 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


Oregon State University

6. 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 April 24, 2019. http://hdl.handle.net/1957/12548.

MLA Handbook (7th Edition):

Hao, Guohua. “Efficient training and feature induction in sequential supervised learning.” 2009. Web. 24 Apr 2019.

Vancouver:

Hao G. Efficient training and feature induction in sequential supervised learning. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2019 Apr 24]. 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


Oregon State University

7. Mehta, Neville. Hierarchical structure discovery and transfer in sequential decision problems.

Degree: PhD, Computer Science, 2011, Oregon State University

 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)

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 April 24, 2019. http://hdl.handle.net/1957/25199.

MLA Handbook (7th Edition):

Mehta, Neville. “Hierarchical structure discovery and transfer in sequential decision problems.” 2011. Web. 24 Apr 2019.

Vancouver:

Mehta N. Hierarchical structure discovery and transfer in sequential decision problems. [Internet] [Doctoral dissertation]. Oregon State University; 2011. [cited 2019 Apr 24]. 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

8. Liu, Liping. Machine Learning Methods for Computational Sustainability.

Degree: PhD, Computer Science, 2016, Oregon State University

 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)

Subjects/Keywords: machine learning; Machine learning

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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 April 24, 2019. http://hdl.handle.net/1957/59159.

MLA Handbook (7th Edition):

Liu, Liping. “Machine Learning Methods for Computational Sustainability.” 2016. Web. 24 Apr 2019.

Vancouver:

Liu L. Machine Learning Methods for Computational Sustainability. [Internet] [Doctoral dissertation]. Oregon State University; 2016. [cited 2019 Apr 24]. 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


Oregon State University

9. Hostetler, Jesse A. Monte Carlo Tree Search with Fixed and Adaptive Abstractions.

Degree: PhD, Computer Science, 2017, Oregon State University

 Monte Carlo tree search (MCTS) is a class of online planning algorithms for Markov decision processes (MDPs) and related models that has found success in… (more)

Subjects/Keywords: Artificial intelligence; Markov processes

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APA (6th Edition):

Hostetler, J. A. (2017). Monte Carlo Tree Search with Fixed and Adaptive Abstractions. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/60635

Chicago Manual of Style (16th Edition):

Hostetler, Jesse A. “Monte Carlo Tree Search with Fixed and Adaptive Abstractions.” 2017. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/60635.

MLA Handbook (7th Edition):

Hostetler, Jesse A. “Monte Carlo Tree Search with Fixed and Adaptive Abstractions.” 2017. Web. 24 Apr 2019.

Vancouver:

Hostetler JA. Monte Carlo Tree Search with Fixed and Adaptive Abstractions. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/60635.

Council of Science Editors:

Hostetler JA. Monte Carlo Tree Search with Fixed and Adaptive Abstractions. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/60635


Oregon State University

10. Alkaee Taleghan, Majid. Simulator-Defined MDP Planning with Applications in Natural Resource Management.

Degree: PhD, Computer Science, 2017, Oregon State University

 This work is inspired by problems in natural resource management centered on the challenge of invasive species. Computing optimal management policies for maintaining ecosystem sustainable… (more)

Subjects/Keywords: Markov Decision Processes; Markov processes

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APA (6th Edition):

Alkaee Taleghan, M. (2017). Simulator-Defined MDP Planning with Applications in Natural Resource Management. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/60125

Chicago Manual of Style (16th Edition):

Alkaee Taleghan, Majid. “Simulator-Defined MDP Planning with Applications in Natural Resource Management.” 2017. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/60125.

MLA Handbook (7th Edition):

Alkaee Taleghan, Majid. “Simulator-Defined MDP Planning with Applications in Natural Resource Management.” 2017. Web. 24 Apr 2019.

Vancouver:

Alkaee Taleghan M. Simulator-Defined MDP Planning with Applications in Natural Resource Management. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/60125.

Council of Science Editors:

Alkaee Taleghan M. Simulator-Defined MDP Planning with Applications in Natural Resource Management. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/60125


Oregon State University

11. Natarajan, Sriraam. Effective decision-theoretic assistance through relational hierarchical models.

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

 Building intelligent computer assistants has been a long-cherished goal of AI. Many intelligent assistant systems were built and fine-tuned to specific application domains. In this… (more)

Subjects/Keywords: Decision-Theory; Decision support systems

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APA (6th Edition):

Natarajan, S. (2008). Effective decision-theoretic assistance through relational hierarchical models. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/7574

Chicago Manual of Style (16th Edition):

Natarajan, Sriraam. “Effective decision-theoretic assistance through relational hierarchical models.” 2008. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/7574.

MLA Handbook (7th Edition):

Natarajan, Sriraam. “Effective decision-theoretic assistance through relational hierarchical models.” 2008. Web. 24 Apr 2019.

Vancouver:

Natarajan S. Effective decision-theoretic assistance through relational hierarchical models. [Internet] [Doctoral dissertation]. Oregon State University; 2008. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/7574.

Council of Science Editors:

Natarajan S. Effective decision-theoretic assistance through relational hierarchical models. [Doctoral Dissertation]. Oregon State University; 2008. Available from: http://hdl.handle.net/1957/7574


Oregon State University

12. Oberst, Ian. On feature relevance feedback methods : incorporating labeled user features.

Degree: MS, Computer Science, 2010, Oregon State University

 In text classification, labeling features is often less time consuming than labeling entire documents. In situations where very little labeled training data is available, feature… (more)

Subjects/Keywords: feature relevance feedback; Information retrieval

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APA (6th Edition):

Oberst, I. (2010). On feature relevance feedback methods : incorporating labeled user features. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/17411

Chicago Manual of Style (16th Edition):

Oberst, Ian. “On feature relevance feedback methods : incorporating labeled user features.” 2010. Masters Thesis, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/17411.

MLA Handbook (7th Edition):

Oberst, Ian. “On feature relevance feedback methods : incorporating labeled user features.” 2010. Web. 24 Apr 2019.

Vancouver:

Oberst I. On feature relevance feedback methods : incorporating labeled user features. [Internet] [Masters thesis]. Oregon State University; 2010. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/17411.

Council of Science Editors:

Oberst I. On feature relevance feedback methods : incorporating labeled user features. [Masters Thesis]. Oregon State University; 2010. Available from: http://hdl.handle.net/1957/17411


Oregon State University

13. Das, Shubhomoy. Incorporating User Feedback into Machine Learning Systems.

Degree: PhD, 2017, Oregon State University

 Although machine learning systems are often effective in real-world applications, there are situations in which they can be even better when provided with some degree… (more)

Subjects/Keywords: Machine Learning

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APA (6th Edition):

Das, S. (2017). Incorporating User Feedback into Machine Learning Systems. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/61580

Chicago Manual of Style (16th Edition):

Das, Shubhomoy. “Incorporating User Feedback into Machine Learning Systems.” 2017. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/61580.

MLA Handbook (7th Edition):

Das, Shubhomoy. “Incorporating User Feedback into Machine Learning Systems.” 2017. Web. 24 Apr 2019.

Vancouver:

Das S. Incorporating User Feedback into Machine Learning Systems. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/61580.

Council of Science Editors:

Das S. Incorporating User Feedback into Machine Learning Systems. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61580


Oregon State University

14. Lauer, Christopher Joseph. Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming.

Degree: PhD, 2017, Oregon State University

 Forest management in the face of fire risk is a challenging problem because fire spreads across a landscape and because its occurrence is unpredictable. Additionally,… (more)

Subjects/Keywords: reinforcement learning

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APA (6th Edition):

Lauer, C. J. (2017). Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/61678

Chicago Manual of Style (16th Edition):

Lauer, Christopher Joseph. “Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming.” 2017. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/61678.

MLA Handbook (7th Edition):

Lauer, Christopher Joseph. “Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming.” 2017. Web. 24 Apr 2019.

Vancouver:

Lauer CJ. Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/61678.

Council of Science Editors:

Lauer CJ. Determining optimal timber harvest and fuel treatment on a fire-threatened landscape using approximate dynamic programming. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61678

15. McGregor, Sean. Machine learning methods for public policy: simulation, optimization, and visualization.

Degree: PhD, 2017, Oregon State University

 Society faces many complex management problems, particularly in the area of shared public resources such as ecosystems. Existing decision making processes are often guided by… (more)

Subjects/Keywords: Markov Decision Processes

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APA (6th Edition):

McGregor, S. (2017). Machine learning methods for public policy: simulation, optimization, and visualization. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/61702

Chicago Manual of Style (16th Edition):

McGregor, Sean. “Machine learning methods for public policy: simulation, optimization, and visualization.” 2017. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/61702.

MLA Handbook (7th Edition):

McGregor, Sean. “Machine learning methods for public policy: simulation, optimization, and visualization.” 2017. Web. 24 Apr 2019.

Vancouver:

McGregor S. Machine learning methods for public policy: simulation, optimization, and visualization. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/61702.

Council of Science Editors:

McGregor S. Machine learning methods for public policy: simulation, optimization, and visualization. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61702

16. Sorower, Mohammad Shahed. Improving Automated Email Tagging with Implicit Feedback.

Degree: PhD, Computer Science, 2015, Oregon State University

 Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a… (more)

Subjects/Keywords: implicit feedback; Electronic mail messages

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APA (6th Edition):

Sorower, M. S. (2015). Improving Automated Email Tagging with Implicit Feedback. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/57963

Chicago Manual of Style (16th Edition):

Sorower, Mohammad Shahed. “Improving Automated Email Tagging with Implicit Feedback.” 2015. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/57963.

MLA Handbook (7th Edition):

Sorower, Mohammad Shahed. “Improving Automated Email Tagging with Implicit Feedback.” 2015. Web. 24 Apr 2019.

Vancouver:

Sorower MS. Improving Automated Email Tagging with Implicit Feedback. [Internet] [Doctoral dissertation]. Oregon State University; 2015. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/57963.

Council of Science Editors:

Sorower MS. Improving Automated Email Tagging with Implicit Feedback. [Doctoral Dissertation]. Oregon State University; 2015. Available from: http://hdl.handle.net/1957/57963

17. Griffioen, Arwen Twinkle E. Creating, Understanding and Applying Machine Learning Models of Multiple Species.

Degree: PhD, Computer Science, 2015, Oregon State University

 Many problems in ecology and conservation biology can be formulated and solved using machine learning algorithms for multi-label classification. This dissertation addresses three topics related… (more)

Subjects/Keywords: machine learning; Machine learning

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APA (6th Edition):

Griffioen, A. T. E. (2015). Creating, Understanding and Applying Machine Learning Models of Multiple Species. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/56049

Chicago Manual of Style (16th Edition):

Griffioen, Arwen Twinkle E. “Creating, Understanding and Applying Machine Learning Models of Multiple Species.” 2015. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/56049.

MLA Handbook (7th Edition):

Griffioen, Arwen Twinkle E. “Creating, Understanding and Applying Machine Learning Models of Multiple Species.” 2015. Web. 24 Apr 2019.

Vancouver:

Griffioen ATE. Creating, Understanding and Applying Machine Learning Models of Multiple Species. [Internet] [Doctoral dissertation]. Oregon State University; 2015. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/56049.

Council of Science Editors:

Griffioen ATE. Creating, Understanding and Applying Machine Learning Models of Multiple Species. [Doctoral Dissertation]. Oregon State University; 2015. Available from: http://hdl.handle.net/1957/56049

18. Lin, Junyuan. A study of methods for fine-grained object classification of arthropod specimens.

Degree: MS, Computer Science, 2013, Oregon State University

 Object categorization is one of the fundamental topics in computer vision research. Most current work in object categorization aims to discriminate among generic object classes… (more)

Subjects/Keywords: Computer Vision; Computer vision

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APA (6th Edition):

Lin, J. (2013). A study of methods for fine-grained object classification of arthropod specimens. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/38009

Chicago Manual of Style (16th Edition):

Lin, Junyuan. “A study of methods for fine-grained object classification of arthropod specimens.” 2013. Masters Thesis, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/38009.

MLA Handbook (7th Edition):

Lin, Junyuan. “A study of methods for fine-grained object classification of arthropod specimens.” 2013. Web. 24 Apr 2019.

Vancouver:

Lin J. A study of methods for fine-grained object classification of arthropod specimens. [Internet] [Masters thesis]. Oregon State University; 2013. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/38009.

Council of Science Editors:

Lin J. A study of methods for fine-grained object classification of arthropod specimens. [Masters Thesis]. Oregon State University; 2013. Available from: http://hdl.handle.net/1957/38009

19. Payet, Nadia. From shape-based object recognition and discovery to 3D scene interpretation.

Degree: PhD, Computer Science, 2011, Oregon State University

 This dissertation addresses a number of inter-related and fundamental problems in computer vision. Specifically, we address object discovery, recognition, segmentation, and 3D pose estimation in… (more)

Subjects/Keywords: object recognition

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APA (6th Edition):

Payet, N. (2011). From shape-based object recognition and discovery to 3D scene interpretation. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/21316

Chicago Manual of Style (16th Edition):

Payet, Nadia. “From shape-based object recognition and discovery to 3D scene interpretation.” 2011. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/21316.

MLA Handbook (7th Edition):

Payet, Nadia. “From shape-based object recognition and discovery to 3D scene interpretation.” 2011. Web. 24 Apr 2019.

Vancouver:

Payet N. From shape-based object recognition and discovery to 3D scene interpretation. [Internet] [Doctoral dissertation]. Oregon State University; 2011. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/21316.

Council of Science Editors:

Payet N. From shape-based object recognition and discovery to 3D scene interpretation. [Doctoral Dissertation]. Oregon State University; 2011. Available from: http://hdl.handle.net/1957/21316

20. Shen, Jianqiang. Activity recognition in desktop environments.

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

 Knowledge workers are struggling in the information flood. There is a growing interest in intelligent desktop environments that help knowledge workers organize their daily life.… (more)

Subjects/Keywords: machine learning; Human activity recognition  – Data processing

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APA (6th Edition):

Shen, J. (2009). Activity recognition in desktop environments. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/11179

Chicago Manual of Style (16th Edition):

Shen, Jianqiang. “Activity recognition in desktop environments.” 2009. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/11179.

MLA Handbook (7th Edition):

Shen, Jianqiang. “Activity recognition in desktop environments.” 2009. Web. 24 Apr 2019.

Vancouver:

Shen J. Activity recognition in desktop environments. [Internet] [Doctoral dissertation]. Oregon State University; 2009. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/11179.

Council of Science Editors:

Shen J. Activity recognition in desktop environments. [Doctoral Dissertation]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/11179

21. Lam, Michael (Michael Quang Thai). Object detection in biological images using a search-based framework.

Degree: MS, Computer Science, 2014, Oregon State University

 This thesis addresses a basic problem in computer vision, that of semantic labeling of images. Our work is aimed at object detection in biological images… (more)

Subjects/Keywords: Computer Vision; Computer vision

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APA (6th Edition):

Lam, M. (. Q. T. (2014). Object detection in biological images using a search-based framework. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/49275

Chicago Manual of Style (16th Edition):

Lam, Michael (Michael Quang Thai). “Object detection in biological images using a search-based framework.” 2014. Masters Thesis, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/49275.

MLA Handbook (7th Edition):

Lam, Michael (Michael Quang Thai). “Object detection in biological images using a search-based framework.” 2014. Web. 24 Apr 2019.

Vancouver:

Lam M(QT. Object detection in biological images using a search-based framework. [Internet] [Masters thesis]. Oregon State University; 2014. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/49275.

Council of Science Editors:

Lam M(QT. Object detection in biological images using a search-based framework. [Masters Thesis]. Oregon State University; 2014. Available from: http://hdl.handle.net/1957/49275

22. Brendel, William. From multitarget tracking to event recognition in videos.

Degree: PhD, Computer Science, 2011, Oregon State University

 This dissertation addresses two fundamental problems in computer vision—namely, multitarget tracking and event recognition in videos. These problems are challenging because uncertainty may arise from… (more)

Subjects/Keywords: multitarget tracking; Computer vision

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APA (6th Edition):

Brendel, W. (2011). From multitarget tracking to event recognition in videos. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/21315

Chicago Manual of Style (16th Edition):

Brendel, William. “From multitarget tracking to event recognition in videos.” 2011. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/21315.

MLA Handbook (7th Edition):

Brendel, William. “From multitarget tracking to event recognition in videos.” 2011. Web. 24 Apr 2019.

Vancouver:

Brendel W. From multitarget tracking to event recognition in videos. [Internet] [Doctoral dissertation]. Oregon State University; 2011. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/21315.

Council of Science Editors:

Brendel W. From multitarget tracking to event recognition in videos. [Doctoral Dissertation]. Oregon State University; 2011. Available from: http://hdl.handle.net/1957/21315

23. Doppa, Janardhan Rao. Integrating learning and search for structured prediction.

Degree: PhD, Computer Science, 2014, Oregon State University

 We are witnessing the rise of the data-driven science paradigm, in which massive amounts of data - much of it collected as a side-effect of… (more)

Subjects/Keywords: Structured Prediction; Machine learning

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APA (6th Edition):

Doppa, J. R. (2014). Integrating learning and search for structured prediction. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/50908

Chicago Manual of Style (16th Edition):

Doppa, Janardhan Rao. “Integrating learning and search for structured prediction.” 2014. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/50908.

MLA Handbook (7th Edition):

Doppa, Janardhan Rao. “Integrating learning and search for structured prediction.” 2014. Web. 24 Apr 2019.

Vancouver:

Doppa JR. Integrating learning and search for structured prediction. [Internet] [Doctoral dissertation]. Oregon State University; 2014. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/50908.

Council of Science Editors:

Doppa JR. Integrating learning and search for structured prediction. [Doctoral Dissertation]. Oregon State University; 2014. Available from: http://hdl.handle.net/1957/50908

24. Keiser, Victoria L. Evaluating online text classification algorithms for email prediction in TaskTracer.

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

 This paper examines how six online multiclass text classification algorithms perform in the domain of email tagging within the TaskTracer system. TaskTracer is a project-oriented… (more)

Subjects/Keywords: email; Learning classifier systems

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APA (6th Edition):

Keiser, V. L. (2009). Evaluating online text classification algorithms for email prediction in TaskTracer. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/11870

Chicago Manual of Style (16th Edition):

Keiser, Victoria L. “Evaluating online text classification algorithms for email prediction in TaskTracer.” 2009. Masters Thesis, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/11870.

MLA Handbook (7th Edition):

Keiser, Victoria L. “Evaluating online text classification algorithms for email prediction in TaskTracer.” 2009. Web. 24 Apr 2019.

Vancouver:

Keiser VL. Evaluating online text classification algorithms for email prediction in TaskTracer. [Internet] [Masters thesis]. Oregon State University; 2009. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/11870.

Council of Science Editors:

Keiser VL. Evaluating online text classification algorithms for email prediction in TaskTracer. [Masters Thesis]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/11870

25. Xie, Jun. Learning greedy policies for the easy-first framework.

Degree: MS, Computer Science, 2014, Oregon State University

 Easy-first, a search-based structured prediction approach, has been applied to many NLP tasks including dependency parsing and coreference resolution. This approach employs a learned greedy… (more)

Subjects/Keywords: Structured Prediction; Machine learning

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

APA (6th Edition):

Xie, J. (2014). Learning greedy policies for the easy-first framework. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/46790

Chicago Manual of Style (16th Edition):

Xie, Jun. “Learning greedy policies for the easy-first framework.” 2014. Masters Thesis, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/46790.

MLA Handbook (7th Edition):

Xie, Jun. “Learning greedy policies for the easy-first framework.” 2014. Web. 24 Apr 2019.

Vancouver:

Xie J. Learning greedy policies for the easy-first framework. [Internet] [Masters thesis]. Oregon State University; 2014. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/46790.

Council of Science Editors:

Xie J. Learning greedy policies for the easy-first framework. [Masters Thesis]. Oregon State University; 2014. Available from: http://hdl.handle.net/1957/46790

26. Xu, Yuehua. Learning ranking functions for efficient search.

Degree: PhD, Computer Science, 2010, Oregon State University

 This dissertation explores algorithms for learning ranking functions to efficiently solve search problems, with application to automated planning. Specifically, we consider the frameworks of beam… (more)

Subjects/Keywords: machine learning; Machine learning

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APA (6th Edition):

Xu, Y. (2010). Learning ranking functions for efficient search. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/17553

Chicago Manual of Style (16th Edition):

Xu, Yuehua. “Learning ranking functions for efficient search.” 2010. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/17553.

MLA Handbook (7th Edition):

Xu, Yuehua. “Learning ranking functions for efficient search.” 2010. Web. 24 Apr 2019.

Vancouver:

Xu Y. Learning ranking functions for efficient search. [Internet] [Doctoral dissertation]. Oregon State University; 2010. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/17553.

Council of Science Editors:

Xu Y. Learning ranking functions for efficient search. [Doctoral Dissertation]. Oregon State University; 2010. Available from: http://hdl.handle.net/1957/17553

27. Judah, Kshitij. New learning modes for sequential decision making.

Degree: PhD, Computer Science, 2014, Oregon State University

 This thesis considers the problem in which a teacher is interested in teaching action policies to computer agents for sequential decision making. The vast majority… (more)

Subjects/Keywords: Sequential Decision Making; Machine learning

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APA (6th Edition):

Judah, K. (2014). New learning modes for sequential decision making. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/47464

Chicago Manual of Style (16th Edition):

Judah, Kshitij. “New learning modes for sequential decision making.” 2014. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/47464.

MLA Handbook (7th Edition):

Judah, Kshitij. “New learning modes for sequential decision making.” 2014. Web. 24 Apr 2019.

Vancouver:

Judah K. New learning modes for sequential decision making. [Internet] [Doctoral dissertation]. Oregon State University; 2014. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/47464.

Council of Science Editors:

Judah K. New learning modes for sequential decision making. [Doctoral Dissertation]. Oregon State University; 2014. Available from: http://hdl.handle.net/1957/47464

28. Dereszynski, Ethan W. Probabilistic models for quality control in environmental sensor networks.

Degree: PhD, Computer Science, 2012, Oregon State University

 Networks of distributed, remote sensors are providing ecological scientists with a view of our environment that is unprecedented in detail. However, these networks are subject… (more)

Subjects/Keywords: Bayesian Networks; Sensor networks  – Quality control

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APA (6th Edition):

Dereszynski, E. W. (2012). Probabilistic models for quality control in environmental sensor networks. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/30896

Chicago Manual of Style (16th Edition):

Dereszynski, Ethan W. “Probabilistic models for quality control in environmental sensor networks.” 2012. Doctoral Dissertation, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/30896.

MLA Handbook (7th Edition):

Dereszynski, Ethan W. “Probabilistic models for quality control in environmental sensor networks.” 2012. Web. 24 Apr 2019.

Vancouver:

Dereszynski EW. Probabilistic models for quality control in environmental sensor networks. [Internet] [Doctoral dissertation]. Oregon State University; 2012. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/30896.

Council of Science Editors:

Dereszynski EW. Probabilistic models for quality control in environmental sensor networks. [Doctoral Dissertation]. Oregon State University; 2012. Available from: http://hdl.handle.net/1957/30896


Oregon State University

29. Ashenfelter, Adam J. Sequential supervised learning and conditional random fields.

Degree: MS, Computer Science, 2003, Oregon State University

 Supervised learning is concerned with discovering the relationship between example sets of features and their corresponding classes. The traditional supervised learning formulation assumes that all… (more)

Subjects/Keywords: Supervised learning (Machine learning)

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APA (6th Edition):

Ashenfelter, A. J. (2003). Sequential supervised learning and conditional random fields. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/10165

Chicago Manual of Style (16th Edition):

Ashenfelter, Adam J. “Sequential supervised learning and conditional random fields.” 2003. Masters Thesis, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/10165.

MLA Handbook (7th Edition):

Ashenfelter, Adam J. “Sequential supervised learning and conditional random fields.” 2003. Web. 24 Apr 2019.

Vancouver:

Ashenfelter AJ. Sequential supervised learning and conditional random fields. [Internet] [Masters thesis]. Oregon State University; 2003. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/10165.

Council of Science Editors:

Ashenfelter AJ. Sequential supervised learning and conditional random fields. [Masters Thesis]. Oregon State University; 2003. Available from: http://hdl.handle.net/1957/10165


Oregon State University

30. Kelm, Bernd Michael. Demosaicking of color images by means of conditional random fields.

Degree: MS, Computer Science, 2003, Oregon State University

 Modern digital still cameras are equipped with just a single CCD for color image acquisition. Since only one spectral band can be recorded in each… (more)

Subjects/Keywords: Digital cameras

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APA (6th Edition):

Kelm, B. M. (2003). Demosaicking of color images by means of conditional random fields. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/10223

Chicago Manual of Style (16th Edition):

Kelm, Bernd Michael. “Demosaicking of color images by means of conditional random fields.” 2003. Masters Thesis, Oregon State University. Accessed April 24, 2019. http://hdl.handle.net/1957/10223.

MLA Handbook (7th Edition):

Kelm, Bernd Michael. “Demosaicking of color images by means of conditional random fields.” 2003. Web. 24 Apr 2019.

Vancouver:

Kelm BM. Demosaicking of color images by means of conditional random fields. [Internet] [Masters thesis]. Oregon State University; 2003. [cited 2019 Apr 24]. Available from: http://hdl.handle.net/1957/10223.

Council of Science Editors:

Kelm BM. Demosaicking of color images by means of conditional random fields. [Masters Thesis]. Oregon State University; 2003. Available from: http://hdl.handle.net/1957/10223

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