paper available: maximum entropy discrimination

[Tommi Jaakkola]

The following technical report (MIT AITR-1668) is now 
available on-line

            Maximum entropy discrimination 

            Jaakkola T., Meila M., Jebara T.

We present a general framework for discriminative estimation based on
the maximum entropy principle and its extensions. All calculations
involve distributions over structures and/or parameters rather than
specific settings and reduce to relative entropy projections. This holds
even when the data is not separable within the chosen parametric class,
in the context of anomaly detection rather than classification, or when
the labels in the training set are uncertain or incomplete. Support
vector machines are naturally subsumed under this class and we provide
several extensions. We are also able to estimate exactly and efficiently
discriminative distributions over tree structures of class-conditional
models within this framework. Preliminary experimental results are
indicative of the potential in these techniques.

http://www.ai.mit.edu/~tommi/publications/maxent.ps.gz

26 pages, about 400KB compressed. 

Tommi
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Tommi Jaakkola                           
MIT Artificial Intelligence Laboratory
545 Technology Square, NE43-735          
Cambridge, MA 02139                      

Tel: (617) 253 0440
Fax: (617) 253 5060
http://www.ai.mit.edu/~tommi
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