Artificial Intelligence Research Seminar Artificial Intelligence Research Laboratory Department of Computer Science Iowa State University Summer 2003

Artificial Intelligence Research Seminar Com S 610 (VH) Summer 2003 will meet once a week on Wednesdays from 2pm to 4pm in room 217, Atanasoff. AI seminar will be coordinated by Vasant Honavar, Dimitris Margaritis, and Jin Tian.

SEMINAR TOPICS

Naive Bayes and Tree-Augmented Naive Bayes Classifiers

Background Material

• Chapter 6 from: Mitchell, T. 1997. Machine Learning. New York: McGraw Hill.
• Com S 573 Lecture notes on Bayesian Learning. Vasant Honavar

• P. Domingos and M. Pazzani. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29:103--130, 1997.
• Bayesian Network Classifiers Friedman, N., Geiger, D., and Goldszmidt, M. Machine Learning 29: pp. 131-163. 1997.
• Building Classifiers Using Bayesian NetworksFriedman, N. and Goldszmidt, M. AAAI 96.
• Tractable Learning of Tree Augmented Naive Bayes ClassifiersCerquides, J. and Lopez de Mantaras, R. Technical Report TR-2003-04, Artificial Intelligence Research Institute, Universitat Autonoma de Barcelona, Bellatora, Spain.

Learning Attribute Value Taxonomies

Readings

• Pereira, Fernando, Naftali Tishby, and Lillian Lee. 1993. Distributional clustering of English words.. Proceedings of the 31st Annual Meeting of the Association for Computational Linguistics, pages 183--190.
• N. Slonim and N. Tishby. Agglomerative information bottleneck.. In Proc. of NIPS-12, pages 617-623. MIT Press, 2000.
• Slonim, N. and Tishby, N. (2000) Document clustering using word clusters via the information bottleneck method. In Proceedings of the 23rd International Conference on Research and Development in Information Retrieval (SIGIR), pp. 208--215.
• T. Hofmann, Probabilistic latent semantic indexing, in Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 1999, pp. 50--57.
• D. J. C. MacKay and L. C. Peto. A hierarchical Language Model.. Natural Language Engineering, 1(3):1--19, 1995.

Noise-Tolerant Learning, Statistical Queries, Property Testing

Background Material

• Chapter 7 from: Mitchell, T. 1997. Machine Learning. New York: McGraw Hill.
• Com S 573 Lecture notes on Computational Learning Theory. Vasant Honavar
• Overview of the Probably Approximately Correct (PAC) Learning Framework. D. Haussler, 1995.
• Computational Learning Theory. Kearns, M. and Vazirani, U. Cambridge, MA: MIT Press.

• Kearrns, M. 1998. Efficient Noise Tolerant Learning from Statistical Queries. Journal of the ACM. Vol. 45, pp. 983-1006.
• Goldreich, O. and Goldwasser, S. 1998. Property testing and its connection to Learning and approximation. Journal of the ACM. Vol. 45. pp. 653-750.
• Cesa-Bianchi, N., Dichterman, E., Fischer, P., Shamir, E., Simon, H. 1999. Sample-Efficient Strategies for Learning in the Presence of Noise. Journal of the ACM. Vol. 46. pp. 684-719.

Clustering

Background Material

• Pavel Berkhin. A Survey of Clustering Algorithms 2002.
• Why so many Clustering Algorithms?

• Pitt, L. and Reinke, R. 1988. Criteria for Polynomial Time (Conceptual) Clustering. Machine Learning. 2:371-396, 1988.
• Barbara, D. 2002. Requirements for Clustering Data Streams. SIGKDD Explorations. Vol. 3. pp. 23-27.
• Mishra, N., Oblinger, D., and Pitt, L. Sublinear Time Approximate Clustering.. 12th Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 439-447, January, 2001.
• Ramgopal R. Mettu and C. Greg Plaxton. Optimal Time Bounds for Approximate Clustering. In Proceedings of the 18th Conference on Uncertainty in Artificial Intelligence, August 2002, pages 344-351.
• P. Indyk, 1999. Sublinear Time Algorithms for Metric Space Problems, Symposium on Theory of Computing (STOC '99).

For additions and updates to this page, please contact: honavar@cs.iastate.edu.

Artificial Intelligence Research Laboratory
Department of Computer Science
Iowa State University
Atanasoff Hall, Ames, IA 50011-1040 USA
phone: +1-515-294-4377, fax: +1-515-294-0258