Thomas G. Dietterich
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Thomas G. Dietterich is emeritus professor of computer science at Oregon State University. He is one of the pioneers of the field of machine learning.[1][2] He served as executive editor of Machine Learning (journal) (1992–98) and helped co-found the Journal of Machine Learning Research.[1] In response to the media's attention on the dangers of artificial intelligence, Dietterich has been quoted for an academic perspective to a broad range of media outlets including National Public Radio, Business Insider, Microsoft Research, CNET, and The Wall Street Journal.[3]
Thomas G. Dietterich | |
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Born | 1954 |
Nationality | American |
Known for | Executive Editor of Machine Learning (journal) (1992–98) |
Academic background | |
Alma mater | Naperville Central High School Oberlin College University of Illinois, Urbana-Champaign Stanford University |
Thesis | “Constraint-Propagation Techniques for Theory-Driven Data Interpretation” (1984) |
Doctoral advisor | Bruce G. Buchanan |
Academic work | |
Institutions | Oregon State University |
Among his research contributions were the invention of error-correcting output coding to multi-class classification, the formalization of the multiple-instance problem, the MAXQ framework for hierarchical reinforcement learning,[1] and the development of methods for integrating non-parametric regression trees into probabilistic graphical models.