OREGON STATE UNIVERSITY

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Biblio

Found 108 results
Filters: Author is Dietterich, Thomas G.  [Clear All Filters]
2003
Valentini, G., and T. G. Dietterich, "Low Bias Bagged Support Vector Machines", International Conference on Machine Learning, ICML-2003, Washington, DC, Morgan Kaufmann, pp. 752–759, 08/2003.
Dietterich, T. G., "Machine Learning ", Nature Encyclopedia of Cognitive Science, London, Macmillan, 2003.
Wang, X., and T. G. Dietterich, "Model-based Policy Gradient Reinforcement Learning", International Conference on Machine Learning, ICML-2003, Washington, DC, pp. 776-783, 08/2003.
2002
Dietterich, T. G., D. Busquets, R L. de Màntaras, and C. Sierra, "Action Refinement in Reinforcement Learning by Probability Smoothing", Proceedings of the Nineteenth International Conference on Machine Learning, Sydney, Australia, Morgan Kaufmann Publishers Inc., pp. 107–114, 07/2002.
Dietterich, T. G., S. Becker, and Z. Ghahramani, Advances in Neural Information Processing Systems 14, , Cambridge, MA., MIT Press, 09/2002.
Dietterich, T. G., and X. Wang, "Batch Value Function Approximation via Support Vectors", Advances in Neural Information Processing Systems 14: MIT Press, pp. 1491-1498, 12/2002.
Valentini, G., and T. G. Dietterich, "Bias-Variance Analysis and Ensembles of SVM", Third International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science, vol. 2364, Naples, Italy, Springer Verlag, pp. 222-231, 06/2002.
Fountain, T., T. G. Dietterich, and B. Sudyka, "Data mining for manufacturing control: an application in optimizing IC tests", Exploring Artificial Intelligence in the New Millenium, San Francisco, CA, Morgan Kaufmann Publishers Inc., pp. 381–400, 2002.
Dietterich, T. G., "Ensemble Learning", The Handbook of Brain Theory and Neural Networks, Second edition, Cambridge, MA, The MIT Press, pp. 405-408, 2002.
Margineantu, D. D., and T. G. Dietterich, "Improved class probability estimates from decision tree models", Nonlinear Estimation and Classification; Lecture Notes in Statistics, vol. 171, New York, Springer-Verlag, pp. 169-184, 2003, 2002.
Dietterich, T. G., "Machine Learning for Sequential Data: A Review", Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition, Ontario, Canada, Springer-Verlag, pp. 15–30, 08/2002.
Zubek, V B., and T. G. Dietterich, "Pruning Improves Heuristic Search for Cost-Sensitive Learning", Proceedings of the Nineteenth International Conference on Machine Learning, Sydney, Australia, Morgan Kaufmann Publishers Inc., pp. 19–26, 07/2002.
Busquets, D., R L. de Màntaras, C. Sierra, and T. G. Dietterich, "Reinforcement Learning for Landmark-based Robot Navigation", In Proc. of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), Bologna, Italy, ACM Press, pp. 841–843, 07/2002.
Wang, X., and T. G. Dietterich, "Stabilizing value function approximation with the BFBP algorithm", Advances in Neural Information Processing Systems 14: MIT Press, pp. 1587-1594, 12/2002.
2001
Bakiri, G., and T. G. Dietterich, "Achieving high-accuracy text-to-speech with machine learning", Data mining in speech synthesis, Boston, MA, Kluwer Academic Publishers, 2001.
Leen, T. K., T. G. Dietterich, and V. Tresp, Advances in Neural Information Processing Systems 13, , Cambridge, MA, pp. MIT Press, 2001.
Margineantu, D. D., and T. G. Dietterich, "Lazy Class Probability Estimators", 33rd Symposium on the Interface of Computing Science and Statistics, Costa Mesa, California, 06/2001.
Zubek, V B., and T. G. Dietterich, "Two Heuristics for Solving POMDPs Having a Delayed Need to Observe", Proceedings of the IJCAI Workshop on Planning under Uncertainty and Incomplete Information, Seattle, WA, 08/2001.
2000
Margineantu, D. D., and T. G. Dietterich, "Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers", Proceedings of the Seventeenth International Conference on Machine Learning, San Francisco, CA, Morgan Kaufmann Publishers Inc., pp. 583–590, 07/2000.
Chown, E., and T. G. Dietterich, "A Divide and Conquer Approach to Learning from Prior Knowledge", Proceedings of the Seventeenth International Conference on Machine Learning, San Francisco, CA, Morgan Kaufmann Publishers Inc., pp. 143–150, 07/2000.
Dietterich, T. G., "The Divide-and-Conquer Manifesto", Proceedings of the 11th International Conference on Algorithmic Learning Theory, London, UK, Springer-Verlag, pp. 13–26, 12/2000.
Wang, X., and T. G. Dietterich, "Efficient Value Function Approximation Using Regression Trees", In Proceedings of the IJCAI Workshop on Statistical Machine Learning for Large-Scale Optimization, pp. 51-54, 2000.
Dietterich, T. G., "Ensemble Methods in Machine Learning", First International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science, Cagliari, Italy, Springer-Verlag, pp. 1–15, 06/2000.
Dietterich, T. G., "An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization", Machine Learning, vol. 40, issue 2, pp. 139 - 157, 08/2000.
Dietterich, T. G., "Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition", Journal of Artificial Intelligence Research - JAIR, vol. 13, pp. 227-303, 2000.

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