Publications by Thomas G. Dietterich

Marx, Z., Rosenstein, M. T., Dietterich, T. G., Kaelbling, L. P. (in press). Two Algorithms for Transfer Learning. To appear in Inductive Transfer: 10 years later. PDF Preprint.

Chown, E., Dietterich, T. G. (submitted). A comparison of neural network and process-based models for vegetation distribution under global climate change. Postscript preprint.

Dietterich, T. G., Domingos, P., Getoor, L., Muggleton, S. Tadepalli, P. (2008). Structured machine learning: the next ten years. Machine Learning. 73(1) 3--23. DOI: 10.1007/s10994-008-5079-1 PDF Preprint.

Wynkoop, M., Dietterich, T. (2008). Learning MDP Action Models Via Discrete Mixture Trees. In Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science Volume 5212/2008, 597-612. Berlin: Springer. PDF Preprint.

Mehta, N., Ray, S., Tadepalli, P., Dietterich, T. (2008). Automatic Discovery and Transfer of MAXQ Hierarchies. International Conference on Machine Learning (ICML-2008) PDF Preprint.

Dietterich, T. G., Bao, X. (2008). Integrating Multiple Learning Components Through Markov Logic. Twenty-Third Conference on Artificial Intelligence (AAAI-2008). PDF Preprint.

Larios, N., Deng, H., Zhang, W., Sarpola, M., Yuen, J., Paasch, R., Moldenke, A., Lytle, D., Ruiz Correa, S., Mortensen, E., Shapiro, L., Dietterich, T. (2008). Automated Insect Identication through Concatenated Histograms of Local Appearance Features. Machine Vision and Applications, 19 (2):105-123. PDF Preprint.

Stumpf, S., Fitzhenry, E., Dietterich, T. (2007). The Use of Provenance in Information Retrieval.. Workshop on Principles of Provenance (PROPR), Edinburgh, Scotland, 19-20 November, 2007. PDF Preprint.

Dietterich, T. G. (2007). Machine Learning in Ecosystem Informatics. Proceedings of the Tenth International Conference on Discovery Science. Lecture Notes in Artificial Intelligence Volume 4755, Springer, Berlin. PDF Preprint.

Peterson, C., Paasch, R. K., Ge, P., Dietterich, T. G. (2007). Product innovation for interdisciplinary design under changing requirements. International Conference on Engineering Design (ICED2007), Paris, France. PDF preprint.

Dereszynski, E., Dietterich, T. (2007). Probabilistic Models for Anomaly Detection in Remote Sensor Data Streams. Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence (UAI-2007). 75-82. PDF preprint.

E. N. Mortensen, E. L. Delgado, H. Deng, D. Lytle, A. Moldenke, R. Paasch, L. Shapiro, P. Wu, W. Zhang, T. G. Dietterich (2007). Pattern Recognition for Ecological Science and Environmental Monitoring: An Initial Report. In N. MacLeod and M. O'Neill (Eds.) Automated Taxon Identification in Systematics. 189-206. CRC Press, Boca Raton. PDF preprint.

Deng, H., Zhang, W., Mortensen, E., Dietterich, T. (2007). Principal Curvature-based Region Detector for Object Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR-2007). Minneapolis, MN. PDF Preprint.

Larios, N., Deng, H., Zhang, W., Sarpola, M., Yuen, J., Paasch, R., Moldenke, A., Lytle, D., Ruiz Correa, S., Mortensen, E., Shapiro, L. G., Dietterich T. G. (2007). Automated Insect Identification through Concatenated Histograms of Local Appearance Features. IEEE Workshop on Applications of Computer Vision (WACV-2007), 26-32. Austin, TX. PDF Preprint.

Stumpf, S., Rajaram, V., Li, L., Burnett, M., Dietterich, T., Sullivan, E., Drummond, R., Herlocker, J. (2007). Toward Harnessing User Feedback for Machine Learning. In International Conference on Intelligent User Interfaces (IUI-2007), Honolulu, HI. pp. 82-91. ACM Press. PDF Preprint.

Shen, J., Dietterich, T. (2007). Active EM to Reduce Noise in Activity Recognition. In International Conference on Intelligent User Interfaces (IUI-2007), Honolulu, HI. pp. 132-140. ACM Press. PDF Preprint.

Shen, J., Li, L., Dietterich, T. G. (2007). Real-Time Detection of Task Switches of Desktop Users. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-07). Hyderabad, India. pp. 2868-2873. PDF preprint.

Deng, H., Mortensen, E. N., Shapiro, L., Dietterich, T. G. (2006). Reinforcement Matching Using Region Context. In S. Lucey and T. Chen (Eds.) Beyond Patches. Workshop at IEEE Conference on Computer Vision and Pattern Recognition. IEEE. New York. PDF preprint.

Zhang, W., Deng, H., Dietterich, T. G., Mortensen, E. N. (2006). A Hierarchical Object Recognition System Based on Multi-scale Principal Curvature Regions. Proceedings of the International Conference on Pattern Recognition, Vol. I. 778-782. PDF Preprint.

Langford, W. T., Gergel, S. E., Dietterich, T. G., Cohen, W. (2006). Map misclassification can cause large errors in landscape pattern indices: Examples from habitat fragmentation. Ecosystems, 9 (3), 474-488. PDF Preprint.

Bao, X., Herlocker, J., Dietterich, T. (2006). Fewer clicks and less frustration: Reducing the cost of reaching the right folder. In 2006 International Conference on Intelligent User Interfaces. 178-185. Sydney, Australia. PDF preprint.

Shen, J., Li, L., Dietterich, T., Herlocker, J. (2006). A Hybrid Learning System for Recognizing User Tasks from Desktop Activities and Email Messages. In 2006 International Conference on Intelligent User Interfaces. 86-92. Sydney, Australia. PDF postprint. (Corrected bibliography after publication.)

Marx, Z., Rosenstein, M. T., Kaelbling, L. P., Dietterich, T. G. (2005). Transfer learning with an ensemble of background tasks. NIPS 2005 Workshop on Transfer Learning, Whistler, BC. PDF preprint.

Rosenstein, M. T., Marx, Z., Kaelbling, L. P., Dietterich, T. G. (2005). To transfer or not to transfer. NIPS 2005 Workshop on Transfer Learning, Whistler, BC. PDF preprint.

Altendorf, E., Restificar, E., Dietterich, T. G. (2005). Learning from sparse data by exploiting monotonicity constraints. Proceedings of the 21st Conference on Uncertainty in Artificial Intelligence, Edinburgh, Scotland. PDF preprint.

Natarajan, S., Tadepalli, P., Altendorf, E., Dietterich, T. G., Fern, A., Restificar, A. (2005). Learning first-order probabilistic models with combining rules. Proceedings of the 22nd International Conference on Machine Learning, Bonn, Germany. PDF preprint.

Bayer-Zubek, V., Dietterich, T. G. (2005). Integrating Learning from Examples into the Search for Diagnostic Policies. Journal of Artificial Intelligence Research, 24, 263-303. JAIR web page.

Stumpf, S., Bao, X., Dragunov, A., Dietterich, T., Herlocker, J., Johnsrude, K., Li, L., Shen, J. (2005). Predicting User Tasks: I Know What You're Doing!. In Workshop on Human Comprehensible Machine Learning. Twentieth National Conference on Artificial Intelligence (AAAI-05). Pittsburgh, PA, July 9-13, 2005. PDF preprint.

Dragunov, A. N., Dietterich, T. G., Johnsrude, K., McLaughlin, M., Li, L., Herlocker, J. L. (2005). TaskTracer: A Desktop Environment to Support Multi-tasking Knowledge Workers. International Conference on Intelligent User Interfaces (IUI-2005), (pp. 75-82). ACM Press. PDF preprint.

Dietterich, T. G. Learning and Reasoning. Technical report, School of Electrical Engineering and Computer Science, Oregon State University. PDF version gzipped postscript version.

Barto, A., Dietterich, T. G. (2004). Reinforcement learning and its relationship to supervised learning. In J. Si, A. G. Barto, W. B. Powell, D. Wunsch II (Eds.) Handbook of Learning and Approximate Dynamic Programming. pp. 47-64. Wiley Interscience/IEEE Press, Piscataway, NJ. PDF preprint.

Dietterich, T. G., Ashenfelter, A., Bulatov, Y. (2004). Training Conditional Random Fields via Gradient Tree Boosting. International Conference on Machine Learning, 217-224, Banff, Canada PDF preprint.

Wu, P., Dietterich, T. G. (2004). Improving SVM Accuracy by Training on Auxiliary Data Sources. International Conference on Machine Learning, 871-878, Banff, Canada PDF preprint.

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. PDF preprint. gzipped postscript preprint.

Valentini, G. and Dietterich, T. G. (2003). Low Bias Bagged Support Vector Machines. International Conference on Machine Learning, ICML-2003, Washington, DC, 752-759. PDF preprint. gzipped postscript.

Wang, X. and Dietterich, T. G. (2003). Model-based Policy Gradient Reinforcement Learning. International Conference on Machine Learning, ICML-2003, Washington, DC, 776-783. Postscript preprint.

Dietterich, T. G. (2003). Machine Learning. In Nature Encyclopedia of Cognitive Science, London: Macmillan, 2003. Postscript Preprint PDF Version

Dietterich, T. G. and Wang, X. (2002). Batch value function approximation via support vectors. In Dietterich, T. G., Becker, S., Ghahramani, Z. (Eds.) Advances in Neural Information Processing Systems 14. (pp. 1491-1498). Cambridge, MA: MIT Press. Postscript preprint.

Wang, X. and Dietterich, T. G. (2002). Stabilizing value function approximation with the BFBP algorithm. In T. G., Becker, S., Ghahramani, Z. (Eds.) Advances in Neural Information Processing Systems 14. (pp. 1587-1594). Cambridge, MA: MIT Press. Postscript preprint.

Margineantu, D. D. and Dietterich, T. G. (2002) Improved class probability estimates from decision tree models. in D. D. Denison, M. H. Hansen, C. C. Holmes, B. Mallick, and B. Yu (Eds.) Nonlinear Estimation and Classification; Lecture Notes in Statistics, 171, pp. 169-184. New York: Springer-Verlag. Postscript preprint. PDF preprint. © Springer-Verlag.

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, Vol. 2396. (pp. 15-30). Springer-Verlag. Postscript preprint. PDF preprint. © Springer-Verlag.

Fountain, T., Dietterich, T., and Sudyka, B. (2002). Data mining for manufacturing control: An application in optimizing IC test. Chapter 13 of B. Nebel and G. Lakemeyer (Eds.) Exploring Artificial Intelligence in the New Millenium. Morgan-Kaufmann. Postscript preprint.

Busquets, D., Lopez de Mantaras, R., Sierra, C., and Dietterich, T. G. (2002). Reinforcement learning for landmark-based robot navigation. Proceedings of Autonomous Agents and Multi-Agent Systems. (pp. 841-842). ACM Press. Postscript (Longer version is available as a technical report; see below)

Dietterich, T. G., Becker, S., and Ghahramani, Z. (eds.) (2002). Advances in Neural Information Processing Systems 14. MIT Press, Cambridge, MA. Online Proceedings.

Dietterich, T. G., Busquets, D., Lopez de Mantaras, R., Sierra, C. (2002). Action Refinement in Reinforcement Learning by Probability Smoothing. In Proceedings of the International Conference on Machine Learning. (pages 107-114) Postscript Preprint. PDF Preprint.

Zubek, V. B., Dietterich, T. G. (2002). Pruning Improves Heuristic Search for Cost-Sensitive Learning. In Proceedings of the International Conference on Machine Learning. (pages 27-34) Postscript Preprint. PDF Preprint.

Dietterich, T. G. (2002). Ensemble Learning. In The Handbook of Brain Theory and Neural Networks, Second edition, (M.A. Arbib, Ed.), Cambridge, MA: The MIT Press, 2002. 405-408. Postscript Preprint.

Valentini, G., Dietterich, T. G. (2002). Bias-Variance Analysis and Ensembles of SVM. In J. Kittler and F. Roli (Ed.) Third International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science, 2364. (pp. 222-231) New York: Springer Verlag. Postscript preprint © Springer-Verlag.

Busquets, D., Lopez de Mantaras, R., Sierra, C., and Dietterich, T. G. (2001). Reinforcement learning for landmark-based robot navigation. Technical Report, Department of Computer Science, Oregon State University. Postscript

Bakiri, G., Dietterich, T. G. (2001). Achieving high-accuracy text-to-speech with machine learning. In B. Damper (Ed.) Data mining in speech synthesis. Kluwer Academic Publishers, Boston, MA. Postscript preprint.

Zubek, V. B., Dietterich, T. G. (2001). Two Heuristics for Solving POMDPs Having a Delayed Need to Observe. To appear in Proceedings of the IJCAI Workshop on Planning under Uncertainty and Incomplete Information. August 6, 2001. Seattle, WA. Postscript preprint.

Margineantu, D. and Dietterich, T. G. (2001). Lazy Class Probability Estimators. In 33rd Symposium on the Interface of Computing Science and Statistics, Costa Mesa, California.

Leen, T. K., Dietterich, T. G., and Tresp, V. (2001) Advances in Neural Information Processing Systems, 13, Cambridge, MA: MIT Press.

Dietterich, T. G. (2000). The Divide-and-Conquer Manifesto In Algorithmic Learning Theory 11th International Conference (ALT 2000) (pp. 13-26). New York: Springer-Verlag. Postscript Preprint. © Springer-Verlag.

Dietterich, T. G. (2000). Hierarchical reinforcement learning with the MAXQ value function decomposition. Journal of Artificial Intelligence Research, 13, 227-303. Compressed postscript. Also available from my HTTP directory as Gzipped postscript

Wang, X., Dietterich, T. G. (2000). Efficient value function approximation using regression trees. Pages 51-54 of collective article: J. Boyan, W. Buntine, and A. Jagota (Eds.), Statistical Machine Learning for Large Scale Optimization. Neural Computing Surveys, 3, 1-58. Gzipped postscript.

Dietterich, T. G. (2000). Machine Learning. In David Hemmendinger, Anthony Ralston and Edwin Reilly (Eds.), The Encyclopedia of Computer Science, Fourth Edition, Thomson Computer Press. 1056-1059.

Zubek, V. B. and Dietterich, T. G. (2000) A POMDP Approximation Algorithm that Anticipates the Need to Observe. In Proceedings of the Pacific Rim Conference on Artificial Intelligence (PRICAI-2000); Lecture Notes in Computer Science (pp. 521-532). New York: Springer-Verlag. Postscript Preprint. © Springer-Verlag.

Fountain, T., Dietterich, T. G., 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. PDF preprint. Winner of Award for Best Application Paper (Research Track).

Dietterich, T. G. (2000). An Overview of MAXQ Hierarchical Reinforcement Learning. In B. Y. Choueiry and T. Walsh (Eds.) Proceedings of the Symposium on Abstraction, Reformulation and Approximation SARA 2000, Lecture Notes in Artificial Intelligence (pp. 26-44), New York: Springer Verlag. Postscript preprint © Springer-Verlag.

Chown, E., Dietterich, T. G. (2000). A Divide-and-Conquer Approach to Learning From Prior Knowledge. In International Conference on Machine Learning, ICML-2000 (pp. 143-150). Postscript preprint.

Margineantu, D. D., Dietterich, T. G. (2000). Bootstrap Methods for the Cost-Sensitive Evaluation of Classifiers . In International Conference on Machine Learning, ICML-2000 (pp. 582-590). Postscript preprint

Dietterich, T. G. (2000). Ensemble Methods in Machine Learning. In J. Kittler and F. Roli (Ed.) First International Workshop on Multiple Classifier Systems, Lecture Notes in Computer Science (pp. 1-15). New York: Springer Verlag. Postscript preprint © Springer-Verlag.

Dietterich, T. G., (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40 (2) 139-158. Postscript preprint PDF preprint.

Dietterich, T. G. (2000). State abstraction in MAXQ hierarchical reinforcement learning. In Advances in Neural Information Processing Systems, 12. S. A. Solla, T. K. Leen, and K.-R. Muller (eds.), 994-1000, MIT Press. Postscript Preprint.

Dietterich, T. G. (1999). Machine Learning. In Rob Wilson and Frank Keil (Eds.) The MIT Encyclopedia of the Cognitive Sciences, MIT Press. 497-498.

Margineantu, D., Dietterich, T. G. (1999). Learning Decision Trees for Loss Minimization in Multi-Class Problems. Technical report 99-30-03. Department of Computer Science, Oregon State University Postscript file.

Wang, X., Dietterich, T. G. (1999). Efficient Value Function Approximation Using Regression Trees. In Proceedings of the IJCAI Workshop on Statistical Machine Learning for Large-Scale Optimization, Stockholm, Sweden. Postscript file.

Dietterich, T. G. (1998). The MAXQ method for hierarchical reinforcement learning. 1998 International Conference on Machine Learning. (118-126). Morgan Kaufmann, San Francisco. Postscript preprint. Note: This version has some errors corrected compared to the version that appears in the proceedings. In particular, Figure 1 is fixed.

Dietterich, T. G., (1998). Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms. Neural Computation, 10 (7) 1895-1924. Postscript preprint. (Revised December 30, 1997).

Zhang, W., Dietterich, T. G. (1997). Solving Combinatorial Optimization Tasks by Reinforcement Learning: A General Methodology Applied to Resource-Constrained Scheduling. Technical Report. Department of Computer Science, Oregon State University. Postscript preprint.

Dietterich, T. G., (1997). Machine Learning Research: Four Current Directions AI Magazine. 18 (4), 97-136. Postscript preprint.

Dietterich, T. G. (1997). Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition. Technical report. Gzipped postscript file.

Dietterich, T. G. (1997). Fundamental Experimental Research in Machine Learning. A section of the document Basic Topics in Experimental Computer Science edited by John McCarthy. HTML version or gzipped postscript version.

Kong, E. G., and Dietterich, T. G. (1997). Probability estimation using error-correcting output coding. IASTED International Conference: Artificial Intelligence and Soft Computing, Banff, Canada. Postscript preprint.

Margineantu, D., Dietterich, T. G. (1997). Pruning Adaptive Boosting. Fourteenth International Conference on Machine Learning. Morgan Kaufmann, San Francisco. 211-218. Postscript preprint.

Tadepalli, P., Dietterich, T. G. (1997). Hierarchical Explanation-Based Reinforcement Learning. Fourteenth International Conference on Machine Learning. Morgan Kaufmann, San Francisco. 358-366. Postscript preprint.

Dietterich, T. G., Flann, N. S. (1997). Explanation-based Learning and Reinforcement Learning: A Unified View. Machine Learning, 28, 169-210. Postscript preprint. . See below for short version that appeared in the 1995 Machine Learning Conference.

Dietterich, T. G., Lathrop, R. H., Lozano-Perez, T. (1997) Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence, 89(1-2), 31-71. Postscript file (The last 3 figures are not available online; Note: this version contains a corrected Table 4, in which the confidence interval for C4 is fixed.)

Dietterich, T. G., (1996). Editorial Machine Learning 24 (2), 1-3. Postscript preprint.

Dietterich, T. G., Kearns, M., Mansour, Y., (1996). Applying the weak learning framework to understand and improve C4.5. Proceedings of the Thirteenth International Conference on Machine Learning, 96-104. Postscript preprint.

Zhang, W., Dietterich, T. G., (1996). High-Performance Job-Shop Scheduling With A Time-Delay TD(lambda) Network. D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo (Eds.) Advances in Neural Information Processing Systems, 8, 1024-1030. Postscript preprint.

Almuallim, H., Dietterich, T. G. (1995). A study of maximal-coverage learning algorithms. In D. Wolpert (Ed.) The Mathematics of Generalization: Proceedings of the SFI/CNLS Workshop on Formal Approaches to Supervised Learning . Reading, MA: Addison-Wesley. 279-314. Institute. Postscript preprint.

Dietterich, T. G. (1995) Overfitting and under-computing in machine learning. Computing Surveys, 27(3), 326-327. Postscript preprint

Wettschereck, D., Dietterich, T. G. (1995) An experimental comparison of the nearest-neighbor and nearest-hyperrectangle algorithms. Machine Learning, 19(1), 5-28. Postscript preprint.

Dietterich, T. G., Hild, H., and Bakiri, G., (1995) A comparison of ID3 and backpropagation for English text-to-speech mapping. Machine Learning, 18(1), 51-80. Postscript preprint.

Dietterich, T. G., Bakiri, G. (1995) Solving Multiclass Learning Problems via Error-Correcting Output Codes. Journal of Artificial Intelligence Research 2: 263-286. Postscript file.

Zhang, W., Dietterich, T. G., (1995). A Reinforcement Learning Approach to Job-shop Scheduling. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. (1114-1120). San Francisco, CA: Morgan-Kaufmann. Postscript preprint.

Dietterich, T. G., Flann, N. S., (1995). Explanation-based Learning and Reinforcement Learning: A Unified View. In Proceedings of the 12th International Conference on Machine Learning (pp. 176-184). Tahoe City, CA. San Francisco: Morgan Kaufmann. Poscript preprint. See above for journal version.

Kong, E. B., Dietterich, T. G., (1995). Error-Correcting Output Coding Corrects Bias and Variance. In Proceedings of the 12th International Conference on Machine Learning (pp. 313-321). Tahoe City, CA. San Francisco: Morgan Kaufmann. Poscript preprint.

Zhang, W., Dietterich, T. G., (1995). Value Function Approximations and Job-Shop Scheduling. In J. A. Boyan, A. W. Moore, and R. S. Sutton (Eds.) Proceedings of the Workshop on Value Function Approximation. Carnegie-Mellon University, School of Computer Science, Report Number CMU-CS-95-206. Postscript file.

Dietterich, T. G., Kong, E. B., (1995). Machine Learning Bias, Statistical Bias, and Statistical Variance of Decision Tree Algorithms. Technical Report. Department of Computer Science, Oregon State University. Poscript file.

Jain, A. N., Dietterich, T. G., Lathrop, R. H., Chapman, D., Critchlow, R. E., Bauer, B. E., Webster, T. A., Lozano-Perez, T. (1994). Compass: A shape-based machine learning tool for drug design. Computer-Aided Molecular Design, 8 (6) 635-652.

Almuallim, H., Dietterich, T. G. (1994) Learning boolean concepts in the presence of many irrelevant features. Artificial Intelligence, 69(1-2): 279-306. Postscript preprint.

Dietterich, T. G., Jain, A., Lathrop, R., Lozano-Perez, T. (1994). A comparison of dynamic reposing and tangent distance for drug activity prediction. Advances in Neural Information Processing Systems, 6. San Mateo, CA: Morgan Kaufmann. 216-223. Postscript preprint.

Wettschereck, D., Dietterich, T. G. (1994). Locally adaptive nearest neighbor algorithms. Advances in Neural Information Processing Systems, 6. San Mateo, CA: Morgan Kaufmann. 184-191. Postscript preprint.

Bakiri, G., Dietterich, T. G. (1993). Performance comparison between human engineered and machine learned letter-to-sound rules for English: A machine learning success story. 18th International Conference on the Applications of Computers and Statistics to Science and Society. Cairo, Egypt. Postscript preprint.

Almuallim, H., Dietterich, T. G. (1992). Efficient algorithms for identifying relevant features. Proceedings of the Ninth Canadian Conference on Artificial Intelligence (pp. 38-45). Vancouver, BC: Morgan Kaufmann. May 11-15. Postscript preprint.

Almuallim, H., Dietterich, T. G. (1992). On learning more concepts. In Proceedings of the Ninth International Conference on Machine Learning, (pp. 11-19), Aberdeen, Scotland: Morgan-Kaufmann. Postscript preprint.

Wettschereck, D., Dietterich, T. G. (1992) Improving the performance of radial basis function networks by learning center locations. In Moody, J. E., Hanson, S. J., and Lippmann, R. P. (Eds.) Advances in Neural Information Processing Systems, 4. (pp. 1133-1140) San Mateo, CA: Morgan Kaufmann. Postscript preprint.

Dietterich, T. G. (1991) Knowledge compilation: Bridging the gap between specification and implementation. IEEE Expert, 6 (2) 80-82. Postscript preprint.

Dietterich, T. G., Bakiri, G. (1991) Error-correcting output codes: A general method for improving multiclass inductive learning programs. Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91) (pp. 572-577). Anaheim, CA: AAAI Press. Postscript preprint.

Almuallim, H., Dietterich, T. G. (1991) Learning with many irrelevant features. Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI-91) (pp. 547-552). Anaheim, CA: AAAI Press. Postscript preprint.

Dietterich, T. G. (1991). Do Hidden Units Implement Error-Correcting Codes? Technical Report. Postscript file

Shavlik, J. and Dietterich, T. G. (1990). Readings in Machine Learning. Morgan Kaufmann Publishers, San Francisco, CA. Introductory Article and Contents. Order from Amazon.

Dietterich, T. G. (1990). Machine Learning. Annual Review of Computer Science, 4: 255-306. Postscript preprint with no figures.

Dietterich, T. G., (1990). Editorial: Exploratory Research in Machine Learning. Machine Learning 5 (1), 5-10. Postscript preprint.

Dietterich, T. G., Hild, H., Bakiri, G. (1990) A comparative study of ID3 and backpropagation for English text-to-speech mapping. Proceedings of the 1990 Machine Learning Conference, Austin, TX. 24-31. Postscript preprint.

Cerbone, G., Dietterich, T. G., (1990) Inductive and numerical methods in knowledge compilation. Proceedings of CRIB-90. Menlo Park, CA: Price Waterhouse.

Flann, N. S., and Dietterich, T. G., (1989) A study of explanation-based methods for inductive learning. Machine Learning, 4 (2), 187-226.

Dietterich, T. G., (1989) Limitations of Inductive Learning. Proceedings of the Sixth International Workshop on Machine Learning, Ithaca, NY. San Mateo, CA: Morgan Kaufmann. 124-128. Postscript preprint.

Ullman, D. G., Dietterich, T. G., and Stauffer, L. A. (1988). A model of the mechanical design process based on empirical data. Artificial Intelligence in Engineering, Design, and Manufacturing, 2 (1), 33-52.

Dietterich, T. G., and Flann, N. S., (1988). An inductive approach to solving the imperfect theory problem. Proceedings of the AAAI Spring Symposium Series: Explanation-based Learning, 42-46.

Koff, C. N., Flann, N. S., and Dietterich, T. G., (1988). A specialized ATMS for equivalence relations. Proceedings of the National Conference on Artificial Intelligence (AAAI-88), St. Paul, MN. Los Altos, CA: Morgan-Kaufmann. 182-187.

Dietterich, T. G., Bennett, J. S. (1988). Varieties of Operationality. Technical Report 88-30-6, Department of Computer Science, Oregon State University. PDF Tech Report.

Ullman, D. G., and Dietterich, T. G. (1987). Toward Expert CAD, ASME, Computers in Mechanical Engineering, 6(3), 56-70.

Ullman, D. G., and Dietterich, T. G. (1987). Mechanical design methodology: Implications on future developments of computer-aided design and knowledge-based systems. Engineering With Computers, (2), 21-29.

Flann, N. S., Dietterich, T. G., and Corpron, D. R., (1987). Forward chaining logic programming with the ATMS. In Proceedings of the National Conference on Artificial Intelligence (AAAI-87), Seattle, WA. Los Altos, CA: Morgan-Kaufmann, 24-29.

Dietterich, T. G., and Ullman, D. G., (1987) FORLOG: A Logic-Based Architecture for Design, in Expert Systems in Computer-Aided Design, North-Holland, 1987, 1-24, and presented at IFIP WG5.2 Working Conference on Expert Systems in Computer-Aided Design, Sydney, Australia, February, 1987. PDF Tech Report.

Dietterich, T. G., and D'Ambrosio, B. (1987). Artificial Intelligence at OSU. Technical Report 87-30-5, Department of Computer Science, Oregon State University. PDF Tech Report.

Dietterich, T. G., and Michalski, R. S., (1986). Learning to Predict Sequences , in Machine Learning: An Artificial Intelligence Approach, Volume II, Michalski, R. S., Carbonell, J., and Mitchell, T. M., (eds.), Palo Alto: Tioga, 63-106.

Dietterich, T. G., (1986). Learning at the knowledge level, Machine Learning, 1(3) 287-316. Postscript preprint.

Dietterich, T. G. (1986). A Knowledge-Level Analysis of Learning Systems. Technical Report 87-30-4, Department of Computer Science, Oregon State University, Corvallis, OR. PDF tech report.

Dietterich, T. G., (1986). Induction: Weak but essential (commentary on Schank, Collins, and Hunter), Behavioral and Brain Sciences, 9 (4), 1986, 654-655. Postscript preprint.

Flann, N. and Dietterich, T. G., (1986). Selecting appropriate representations for learning from examples. In Proceedings of the National Conference on Artificial Intelligence: AAAI-86, Philadelphia, PA. Los Altos, CA: Morgan-Kaufmann, 460-466. PDF of technical report.

Dietterich, T. G., and Bennett, J. S., (1986). The Test Incorporation Hypothesis and the Weak Methods, Technical Report TR 86-30-4, Department of Computer Science, Oregon State University, Corvallis, OR. Postscript version.

Dietterich, T. G., and Bennett, J. S. (1986). The Test Incorporation Theory of Problem Solving, In Proceedings of the Workshop on Knowledge Compilation, Department of Computer Science, Oregon State University, Corvallis, OR. Technical Report. Postscript version.

Dietterich, T. G., (Ed.), (1986). Proceedings of the Workshop on Knowledge Compilation, Technical report, Department of Computer Science, Oregon State University, Corvallis, OR.

Dietterich, T. G., Flann, N. S., and Wilkins, D. C., (1986). A Summary of Machine Learning Papers from IJCAI-85, Machine Learning, 1 (2), 227-242. PDF Preprint.

Dietterich, T. G., and Michalski, R. S., (1985). Discovering patterns in sequences of events, Artificial Intelligence, 25, 187-232.

Dietterich, T. G., (1984). Constraint-Propagation Techniques for Theory-Driven Data Interpretation, Doctoral Dissertation, Rep. No. STAN-CS-84-1030, Department of Computer Science, Stanford University, Stanford, California.

Dietterich, T. G., (1984). Learning about systems that contain state variables, Proceedings of AAAI-84, Austin, Texas, 96-100.

Dietterich, T. G., and Buchanan, B. G., (1983). The role of the critic in learning systems, in Rissland, E. W., Arbib, M., and Selfridge, O., Adaptive Control of Ill-defined Systems, Plenum, 127-148.

Dietterich, T. G., and Michalski, R. S., (1983). A comparative review of selected methods for learning from examples, Chapter 3 of Machine Learning: An Artificial Intelligence Approach, Michalski, R. S., Carbonell, J., and Mitchell, T. M., (eds.), Palo Alto: Tioga, 41-82.

Dietterich, T. G., London, R. L., Clarkson, K., and Dromey, G. (1982). Learning and inductive inference. Chapter XIV in Cohen, P. R., and Feigenbaum, E. A., The Handbook of Artificial Intelligence, Vol. III, 323-512, Los Altos, CA: William Kaufmann.

Dietterich, T. G., and Michalski, R. S. (1981). Inductive learning of structural descriptions: Evaluation criteria and comparative review of selected methods. Artificial Intelligence, 16, 257-294.

Dietterich, T. G., (1980). Applying general induction methods to the card game Eleusis, Proceedings of the National Conference on Artificial Intelligence, AAAI-80, Stanford, California, 218-220. Scanned PDF.

Dietterich, T. G., (1980). The Methodology of Knowledge Layers for Inducing Descriptions of Sequentially Ordered Events, Master's Thesis, Rep. No. UIUCDCS-1024, Department of Computer Science, University of Illinois, Urbana, Illinois.

Dietterich, T. G., and Michalski, R. S., (1979). Learning and generalization of characteristic desciptions. Proceedings of the Sixth International Joint Conference on Artificial Intelligence, Tokyo, Japan, 223-231.