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

Artificial Intelligence Research Seminar Com S 610 (VH) Fall 2003 will meet once a week on Wednesdays from 4:30pm to 6:30pm in room 217, Atanasoff. AI seminar will be coordinated by Vasant Honavar, Adrian Silvescu, and Facundo Bromberg.

SEMINAR TOPICS

Topic: Knowledge Acquisition from the Semantic Web: Learning from Heterogeneous, Distributed, Autonomous Information Sources

Readings

• Hendler, J. Agents and the Semantic Web IEEE Intelligent Systems 2001.
• J. Hendler and B. Parsia. XML and the Semantic Web XML Journal, Oct 2002.
• Reinoso-Castillo, J., Silvescu, A., Caragea, D., Pathak, J. and Honavar, V. (2003). Information Extraction and Integration from Heterogeneous, Distributed, Autonomous Information Sources: A Federated, Query-Centric Approach.. IEEE International Conference on Information Integration and Reuse. To appear.
• Zhang, J. and Honavar, V. (2003). Learning Decision Tree Classifiers from Attribute Value Taxonomies and Partially Specified Data. In: Proceedings of the International Conference on Machine Learning (ICML-03). Washington, DC.
• Caragea, D., Reinoso-Castillo, J., Silvescu, A. (2003). Statistics Gathering for Information Integration on the Web. In: Proceedings of the IJCAI-03 Workshop on Information Integration on the Web..
• Caragea, D., Silvescu, A., and Honavar, V. (2003). Decision Tree Induction from Distributed, Heterogeneous, Autonomous Data Sources. In: Proceedings of the Conference on Intelligent Systems Design and Applications (ISDA 03).
• Atramentov, A., Leiva, H., and Honavar, V. (2003). A Multi-Relational Decision Tree Learning Algorithm - Implementation and Experiments.. In: Proceedings of the Thirteenth International Conference on Inductive Logic Programming. Berlin: Springer-Verlag. In press.

Background Material

• Berners-Lee, T., Hendler, J. and Lassila, O. The Semantic Web Scientific American.
• Deborah L. McGuinness. "Ontologies Come of Age". In Dieter Fensel, J im Hendler, Henry Lieberman, and Wolfgang Wahlster, editors. Spinning the Semantic Web: Bringing the World Wide Web to Its Full Potential. MIT Press, 2002.
• The Semantic Web Portal (Contains information about markup languages, ontologies, etc. for the Semantic Web).
• The Gene Ontology
• Caragea, D., Silvescu, A., and Honavar, V. (2001). Invited Chapter. Towards a Theoretical Framework for Analysis and Synthesis of Agents That Learn from Distributed Dynamic Data Sources. In: Emerging Neural Architectures Based on Neuroscience. Berlin: Springer-Verlag.

Topic: Getting Around the Poor Assumptions of Naive Bayes Classifiers

Readings

• Jason D. M. Rennie, Lawrence Shih, Jaime Teevan and David R. Karger Tackling the Poor Assumptions of Naive Bayes Text Classifiers
  Proceedings of the Twentieth International Conference on Machine Learning. 2003.

Background Material

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

Topic: Dealing with Variable Length Inputs in Naive Bayes Classifiers

Readings

• Susana Eyheramendy, David Lewis, David Madigan On the Naive Bayes Model for Text Categorization. In: Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics., Bishop, C.M. and Frey, B. (Ed). 2003.

Background Material

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

Sufficient Statistics from Relational Data

Readings

• Jensen, D. Neville, J. and Hay, M. Avoiding Bias When Aggregating Relational Data With Degree Disparity
  Proceedings of the Twentieth International Conference on Machine Learning. 2003.

Background Material

• Koller, D. Probabilistic Models of Relational Data
• Jensen, D. and J. Neville (2002). Autocorrelation and Linkage Cause Bias in Evaluation of Relational Learners. Proceedings of the 12th International Conference on Inductive Logic Programming.
• Jensen, D. and J. Neville (2002). Linkage and Autocorrelation Cause Feature Selection Bias in Relational Learning.Proceedings of the 19th International Conference on Machine Learning.

Topic: Aggregation Operators for Relational Data

Readings

• Perlich, C. and Provost, F. Aggregation Based Feature Invention for Relational Learning
  Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2003.

Background Material

• Koller, D. Probabilistic Models of Relational Data
• Atramentov, A., Leiva, H., and Honavar, V. (2003). A Multi-Relational Decision Tree Learning Algorithm - Implementation and Experiments.. In: Proceedings of the Thirteenth International Conference on Inductive Logic Programming. Berlin: Springer-Verlag. In press.
• Reinoso-Castillo, J., Silvescu, A., Caragea, D., Pathak, J. and Honavar, V. (2003). Information Extraction and Integration from Heterogeneous, Distributed, Autonomous Information Sources: A Federated, Query-Centric Approach.. IEEE International Conference on Information Integration and Reuse. To appear.

Topic: Learning from Unbalanced Data Sets

Readings

Provost, F. Machine Learning from Imbalanced Data Sets 101
• Weiss, G. and Provost, F. The Effects of Class Distribution on Classifier Learning - An Empirical Study
• Elkan, C. What are the real challenges in data mining? In Workshop on Learning from Imbalanced Data Sets, International Conference on Machine Learning, 2003.
• Maloof, M. Learning When Data Sets are Imbalanced and Costs are Unequal and Unknown In: ICML 2003 Workshop on Learning from Imbalanced Data 2003.

Topic: SVM and Logistic Regression

Readings

• Komarek, P. and Moore, A. Fast Robust Logistic Regression for Large Sparse Datasets with Binary Outputs In: Proceedings of the Ninth International Workshop on Artificial Intelligence and Statistics., Bishop, C.M. and Frey, B. (Ed). 2003.
• Jian Zhang, Rong Jin, Yiming Yang and Alex Hauptmann Modified Logistic Regression: An Approximation to SVM and its Applications in Large-Scale Text Categorization. Proceedings of the Twentieth International Conference on Machine Learning. 2003.

Background Material

• Chapter 6 from: Mitchell, T. 1997. Machine Learning. New York: McGraw Hill.
• Com S 573 Lecture notes on SVM. Vasant Honavar
• An Introduction to Logistic Regression. John Whitehead

Topic: Noise-Tolerant Learning, Statistical Queries, Property Testing

Readings

• Kearns, 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.

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.

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