Reading group: sequence modeling and analysis Fall 2004 Fri 1:00-2:30 PM, CoRE A (301) Fri, 9/10/2004: Segmental models: inference and learning Papers to read: http://www.ai.mit.edu/~murphyk/Bayes/rabiner.pdf http://citeseer.ist.psu.edu/mitchell95complexity.html http://citeseer.ist.psu.edu/90187.html Notes (by Pai-Hsi Huang). Fri, 9/17/2004: Maximum entropy models Papers to read: Stephen Della Pietra, Vincent Della Pietra, John Lafferty, "Inducing Features Of Random Fields," T-PAMI, 1997. A. Berger, "A brief Maxent tutorial" Fri, 9/24/2004: Maximum entropy Markov models Discriminative vs. generative: see a very simple argument on p. 3 (Sec. 3) Choudhury T., Rehg J. M., Pavlovic V. and Pentland A., Boosting and Structure Learning in Dynamic Bayesian Networks for Audio-Visual Speaker Detection", ICPR, 2002. Also: "Bayesian Network Classifiers" N. Friedman, D. Geiger, and M. Goldszmidt, In Machine Learning 29:131--163, 1997. Papers to read: Andrew Mccallum, Dayne Freitag, Fernando Pereira, "Maximum Entropy Markov Models for Information Extraction and Segmentation," ICML 2000. Andrew Mccallum, Dayne Freitag, Fernando Pereira, MEMM Tutorial Fri, 10/1/2004: No reading group Fri, 10/8/2004: Conditional random fields Papers to read: John Lafferty, Andrew McCallum, Fernando Pereira, "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data," ICML 2001.