In automatic speech recognition, we are faced with a wellknown inconsistency: Bayes decision rule is usually used to minimize sentence (word sequence) error, whereas in practice we want to minimize word error, which also is the usual evaluation measure. Recently, a number of speech recognition approaches to approximate Bayes decision rule with word error (Levenshtein/edit distance) cost were pr...

The Pitman closeness ordering of statistical decision rules is considered in the general framework of comparative risk functions. The notions of admis-sibility and of the Bayes rule in this setting (C{admissibility and C{Bayes rule) are introduced. C{admissibility of C{Bayes procedures is demonstrated along with the geometric interpretation of these properties. It is shown that for general comp...

In model-based clustering, a situation in which true class labels are unknown and that is therefore also referred to as unsupervised learning, observations are typically classified by the Bayes modal rule. In this study, we assess whether alternative classifiers from the classification or supervised-learning literature—developed for situations in which class labels are known—can improve the Bay...

We address the problem of training the free parameters of a statistical machine translation system. We show significant improvements over a state-of-the-art minimum error rate training baseline on a large ChineseEnglish translation task. We present novel training criteria based on maximum likelihood estimation and expected loss computation. Additionally, we compare the maximum a-posteriori deci...

A subjective expected utility agent is given information about the state of the world in the form of a set of possible priors. She is allowed to condition her prior on this information. A set of priors may be updated according to Bayes’ rule, prior-by-prior, upon learning that some state of the world has not obtained. We show that there exists no decision maker who obeys Bayes’ rule, conditions...

In this work, fundamental properties of Bayes decision rule using general loss functions are derived analytically and are verified experimentally for automatic speech recognition. It is shown that, for maximum posterior probabilities larger than 1/2, Bayes decision rule with a metric loss function always decides on the posterior maximizing class independent of the specific choice of (metric) lo...

An image classification scheme using Naïve Bayes Classifier is proposed in this paper. The proposed Naive Bayes Classifier-based image classifier can be considered as the maximum a posteriori decision rule. The Naïve Bayes Classifier can produce very accurate classification results with a minimum training time when compared to conventional supervised or unsupervised learning algorithms. Compreh...