In maximum mutual information estimation (MMIE) training, the currently widely used update equations derive from the Extended Baum-Welch (EBW) algorithm, which was originally designed for the discrete hidden Markov model (HMM) and was extended to continuous Gaussian density HMMs through

This paper proposes a new training framework for mixed labeled and unlabeled data and evaluates it on the task of binary phonetic classification. Our training objective function combines Maximum Mutual Information (MMI) for labeled data and Maximum Likelihood (ML) for unlabeled data. Through

Abstract-A discriminative training algorithm to estimate continuous-density hidden Markov model (CDHMM) for automatic speech recognition is considered. The algorithm is based on the criterion, called margin-enhanced maximum mutual information (MEMMI), and it estimates the CDHMM parameters

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A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence

Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech tagging, text segmentation and information extraction. In these cases, the observations are usually modeled

This paper will propose an objective measure based on the information theoretic notion of mutual information, for estimating word association norms from computer readable corpora. (The standard method of obtaining word association norms, testing a few thousand subjects on a few hundred words

A new information-theoretic approach is presented for finding the registration of volumetric medical images of differing modalities. Registration is achieved by adjustment of the relative pose until the mutual information between images is maximized. In our derivation of the registration procedure

(1964), Lintner (1965a) and Treynor (Undated). I apply the measure to estimate the predictive ability of 115 mutual fund managers in the period 1945-1964—that is their ability to earn returns which are higher than those we would expect given the level of risk of each of the portfolios. The foundations

Abstract—Feature selection is an important problem for pattern classification systems. We study how to select good features according to the maximal statistical dependency criterion based on mutual information. Because of the difficulty in directly implementing the maximal dependency condition, we