Part of Advances in Neural Information Processing Systems 14 (NIPS 2001)
Jeff Bilmes, Gang Ji, Marina Meila
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 dif- ference between the true and estimated likelihood ratio, and we analyze this in the Gaussian case. We ﬁnd that the new correction term signif- icantly improves the classiﬁcation results when tested on medium vo- cabulary 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 appro- priate tournament. Lastly, we ﬁnd that intransitivity appears to be a good measure of classiﬁcation conﬁdence.