Exploiting generative models in discriminative classifiers

Advances in Neural Information Processing Systems

Volumn , Issue , 1999, Pages 487-493

  Jaakkola, Torami S ^a   Haussler, David ^b  

^a MASSACHUSETTS INSTITUTE OF TECHNOLOGY   (United States)

^b Santa Cruz   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

HIDDEN MARKOV MODELS; SUPPORT VECTOR MACHINES;

CLASSIFICATION PERFORMANCE; DECISION BOUNDARY; DISCRIMINATIVE CLASSIFIERS; DISCRIMINATIVE METHODS; MISSING INFORMATION; MODEL BASED APPROACH; PROBABILITY MODELS; PROTEIN SEQUENCE ANALYSIS;

SELF ORGANIZING MAPS;

EID: 84898982939     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (7)

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3. T. Hubbard, A. Murzin, S. Brenner, and C. Chothia. SCOP: a structural classification of proteins database. NAR, 25(1):236-9, Jan. 1997.
4. T. S. Jaakkola and D. Haussler. Exploiting generative models in discriminative classifiers. 1998. Revised and extended version. Will be available from http://www.ai.mit.edu/~tommi.
5. D. J. C. MacKay. Introduction to gaussian processes. 1997. Available from http://wol.ra.phy.cam.ac.uk/mackay/.
6. V. Vapnik. The nature of statistical learning theory. Springer-Verlag, 1995.
7. G. Wahba. Spline models for observational data. CBMS-NSF Regional Conference Series in Applied Mathematics, 1990.