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Abstract:
In this work, we introduce an information-theoretic based
correction term to the likelihood ratio classification method for
multiple classes. Under certain conditions, the term is
sufficient for optimally correcting the difference between the
true and estimated likelihood ratio, and we analyze this in the
Gaussian case. We find that the new correction term significantly
improves the classification results when tested on medium
vocabulary speech recognition tasks. Moreover, the addition of
this term makes the class comparisons analogous to an
intransitive game and we therefore use several tournament-like
strategies to deal with this issue. We find that further small
improvements are obtained by using an appropriate tournament.
Lastly, we find that intransitivity appears to be a good measure
of classification confidence.
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