Intransitive Likelihood-Ratio Classifiers
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.