T
Thomas G. Dietterich
Researcher at Oregon State University
Publications - 286
Citations - 58937
Thomas G. Dietterich is an academic researcher from Oregon State University. The author has contributed to research in topics: Reinforcement learning & Markov decision process. The author has an hindex of 74, co-authored 279 publications receiving 51935 citations. Previous affiliations of Thomas G. Dietterich include University of Wyoming & Stanford University.
Papers
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Journal ArticleDOI
Robust artificial intelligence and robust human organizations
TL;DR: In this article, a short note reviews the properties of high-reliability organizations and draws implications for the development of AI technology and the safe application of that technology in high risk applications.
Book ChapterDOI
Bias-Variance Analysis and Ensembles of SVM
TL;DR: It is shown that the bias-variance decomposition offers a rationale to develop ensemble methods using SVMs as base learners, and two directions for developing SVM ensembles are outlined, exploiting the SVM bias characteristics and the biases in the kernel parameters.
Journal ArticleDOI
Segmentation of touching insects based on optical flow and NCuts
Qing Yao,Qing-jie Liu,Thomas G. Dietterich,Sinisa Todorovic,Jeffrey Lin,Diao Guangqiang,Yang Baojun,Jian Tang +7 more
TL;DR: This paper focuses on developing a segmentation method for separating the touching insects in the rice light-trap insect image from the authors' imaging system to automatically identify and count rice pests by photographing them on a glass table.
Journal Article
PAC optimal MDP planning with application to invasive species management
TL;DR: It is shown that the improved confidence intervals and the new search heuristics yield reductions of between 8% and 47% in the number of simulator calls required to reach near-optimal policies.
Journal ArticleDOI
Editorial Exploratory research in machine learning
TL;DR: The goal of this editorial is to emphasize the importance of exploratory research and to encourage the publication of high quality exploratory results in Machine Learning.