DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING
UNIVERSITY OF CALIFORNIA, SAN DIEGO


CSE 254: Seminar on Learning Algorithms

List of Papers


The list of papers here is unfinished.  Each link should be to a web page where the full text of the paper can be found.  In many cases, other interesting papers are on these web pages also.  Participants in the seminar should feel free to propose papers not on the list here, if these other papers are certainly good and worthwhile to present.  The papers listed here are definitely interesting and worhwhile.

Chapter 7 on the Luduan document retrieval system, in Finding Structure in Text, Genome, and Other Symbolic Sequences by Ted E. Dunning, Ph.D. thesis, University of Sheffield, 1998.

Supervised learning of belief net classifiers by Wei Zhou and Russell Greiner.  Technical Report, University of Alberta, 2001.

Adaptive Probabilistic Networks with Hidden Variables by John Binder, Daphne Koller, Stuart Russell, Keiji Kanazawa.  Machine Learning, 1997.

Theoretical Views of Boosting and Applications - Robert E. Schapire (1999)

Freund, Y.: 1999, An adaptive version of the boost by majority algorithm. Proceedings of the Twelfth Annual Conference on Computational Learning Theory.

A Natural Law of Succession (1995)  Eric Sven Ristad

Behzad M. Shahshahani and David A. Landgrebe, "The Effect of Unlabeled Samples in Reducing the Small Sample Size Problem and Mitigating the Hughes Phenomenon," IEEE Transactions on Geoscience and Remote Sensing, Vol. 32, No. 5, pp 1087-1095, September 1994.

Kamal Nigam, Andrew McCallum, Sebastian Thrun and Tom Mitchell. Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2/3). pp. 103-134. 2000.

Andrew McCallum and Kamal Nigam. A Comparison of Event Models for Naive Bayes Text Classification. In AAAI/ICML-98 Workshop on Learning for Text Categorization, pp. 41-48. Technical Report WS-98-05. AAAI Press. 1998

Information Extraction with HMMs and Shrinkage.  Dayne Frietag and Andrew McCallum. AAAI'99 Workshop on Machine Learning for Information Extraction.

Fast Probabilistic Modeling for Combinatorial Optimization, Shumeet Baluja & Scott Davies, AAAI-98.

Shrinking Trees (1990) Trevor Hastie, Daryl Pregibon

R.J. Tibshirani. Regression shrinkage and selection via the lasso. Technical report, University of Toronto, June 1994.