Thomas G. DietterichProfessor |
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PROFESSIONAL PREPARATION
A.B., Oberlin College in Mathematics, 1977
M.S., University of Illinois in Computer Science, 1979
Ph.D., Stanford University in Computer Science, 1984
APPOINTMENTS
Professor, Computer Science, Oregon State University, 1995-.
Associate Professor, Computer Science, Oregon State University, 1988-95.
Assistant Professor, Computer Science, Oregon State University, 1985-88.
PUBLICATIONS (5 most relevant)
Langford, W. T., Gergel, S. E. Gergel, Dietterich, T. G., Cohen, W. (to appear). Map misclassification can cause large errors in landscape pattern indices: Examples from habitat fragmentation. Accepted for publication in Ecosystems.
Valentini, G., Dietterich, T. G. (2004). Bias-variance analysis of Support Vector Machines for the development of SVM-based ensemble methods. Journal of Machine Learning Research, 5, 725-775.
Dietterich, T. G. (2002). Machine Learning for Sequential Data: A Review. In T. Caelli (ed.) Structural, Syntactic, and Statistical Pattern Recognition.. Lecture Notes in Computer Science, Science, Vol. 2396. New York: Springer Verlag (Invited paper). 15-30.
Chown, E., Dietterich, T. G. (2000). A Divide-and-Conquer Approach to Learning From Prior Knowledge. Proceedings of the Seventeenth International Conference on Machine Learning (pp.143-150). San Francisco: Morgan Kaufmann. (This paper describes methods for calibrating a global vegetation model.)
Fountain, T., Dietterich, T. G., and Sudyka, B. (2000). Mining IC Test Data to Optimize VLSI Testing. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (pp. 18-25). ACM Press. Winner of Best Application Paper Award (Research Track). Reprinted as part of the Distinguished Presentations Track, Proceedings of the International Joint Conference on Artificial Intelligence, 2001, Seattle, WA. Extended version in G. Lakemeyer and B. Nebel (eds.), Exploring Artificial Intelligence in the New Millenium, Morgan Kaufmann, 2002.
PUBLICATIONS (5 additional)
Dietterich, T. G. (2000). Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research, 13, 227-303.
Dietterich, T.G. 2000. An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting, and randomization. Machine Learning, 40(2):139-147.
Dietterich, T.G. 1998. Approximate statistical tests for comparing supervised classification learning algorithims. Neural Computation, 10(7), 1895-1924.
Dietterich, T.G., G. Bakiri. 1995. Solving multiclass learning problems via error-correcting output codes. Journal of Artificial Intelligence Research, 2, 263-286.
Dietterich, T.G., 1997. Machine learning research: Four current directions. AI Magazine, 118(4), 97-136.
SYNERGISTIC ACTIVITIES
- Collaboration with climate modeler (Ron Nielson) to develop automated calibration procedure for MAPSS global vegetation model given future climate scenarios.
- President, International Machine Learning Society, 2001-2006.
- Fellow, American Association for Artificial Intelligence
- Fellow, Association for Computing Machinery
CONTRIBUTIONS TO THIS SUMMER INSTITUTE IN ECOINFORMATICS:
co-PI, research and teaching on statistical machine learning (decision trees, neural networks, support vector machines, Bayesian stochastic models), spatial data mining, and pattern recognition with applications to remote sensing, change detection, anomaly detection, and insect population counts for biodiversity assessment.