Machine Leaning

08 / ~ / 2000 to Present

My interest in machine learning has been primarily in probabilistic models for clustering and classification. I'm particularly interested in classification problems where there is very little labeled data.

 

Publications

  • From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering- Procedings of the Nineteenth International Conference on Machine Learning, July 2002

    By Dan Klein, Sepandar D. Kamvar, and Christopher D. Manning

  • Spectral Learning- Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, August 2003

    By Sepandar D. Kamvar, Dan Klein, and Christopher D. Manning

  • Interpreting and Extending Classical Agglomerative Clustering Algorithms using a Model-Based Approach- Procedings of the Nineteenth International Conference on Machine Learning, July 2002

    By Sepandar D. Kamvar, Dan Klein, and Christopher D. Manning

  • Combining Heterogeneous Classifiers for Word-Sense Disambiguation- ACL-2002 Workshop on Word Sense Disambiguation, 2002

    Dan Klein, Kristina Toutanova, H. Tolga Ilhan, Sepandar D. Kamvar, and Christopher D. Manning

  • Inducing Novel Gene-Drug Interactions from the Biomedical Literature- Preprint, December, 2002

    By Sepandar D. Kamvar, Diane E. Oliver, Christopher D. Manning, and Russ B. Altman

  • An Oncology Patient Interface to Medline- Proceedings of the 37th Annual Meeting of the American Society of Clinical Oncology, 2001

    By Elmer Bernstam, Sepandar D. Kamvar, Funda Meric, John Dugan, Steven Chizek, Chris Stave, Olga Troyanskaya, Jeffrey Chang, and Lawrence Fagan

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  • Medline IRaCS: An Information Retrieval and Clustering System for Genomic Knowledge Acquisition- Symposium on Biomedical Computation at Stanford, 2000

    By Sepandar D. Kamvar, Eldar Giladi, Jeanne Loring, and Mike Walker

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Talks

  • Constrained Clustering for Improved Pattern Discovery - ICML, 2002

  • A Probabilistic Interpretation of Agglomerative Clustering - ICML, 2002

  • Matrix Factorizations for Document Clustering and Topic Extraction - Stanford University, 2001

  • Parametric Mixture Models for Document Clustering and Topic Extraction - IBM Almaden Research Center, 2001

  • Text Mining for Medical Knowledge Acquisition - Incyte Genomics, 2000

Demos