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Abstract:
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.
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