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By Hong-kwang Jeff Kuo, Chin-hui Leet, Imed Zitouni and Eric Fosler-lussiert


In this paper we propose a new formulation of minimum verification error training and apply it to the problem of topic verification as an example. In topic verification, a decision is made as to whether a document truly belongs to a particular topic of interest. Such a decision typically depends on a comparison between a model for the desired topic and a model for hackground topics, using a decision threshold. We propose modeling the background topics as a cohort model consisting of a weighted combination of the M closest topics discovered from the training data. The weights and the decision threshold arc optimized using the generalized probabilistic descent algorithm to explicitly minimize the verification error rate, which is defined to he a weighted sum of the Type I (false rejection) and Type 11 (false acceptance) errors

Year: 2013
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