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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 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

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

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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 first

This paper investigates the application of the mutual infor“ criterion to evaluate a set of candidate features and to select an informative subset to be used as input data for a neural network classifier. Because the mutual information measures arbitrary dependencies between random variables

in w-speaker discourse orhus, 1977). Biber se analysed. Erman i-word verbs, etc.) w, Fine, and Pollio a native speaker in every minute of spoken discourse. Collocation is what makes native speakers ' speech idiomatic, fluent and natural. It is also what often renders second language (L2) learners ' speech awkward, unnatural and even odd. Indeed, it has been established that L2 learners have problems with collocation in their written and spoken language (Granger, 1998; Howarth, 1998; Nesselhauf, 2003, 2005). Some have argued that L2 learners rely on creativity and make “overliberal assumptions about

An overview is presented of the medical image processing literature on mutual-information-based registration. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific

expectation and variance of mutual information. The mutual information of two random variables ı and j with joint probabilities {πij} is commonly used in learning Bayesian nets as well as in many other fields. The chances πij are usually estimated by the empirical sampling frequency nij/n leading