In this paper we present a new approach to classifying radiographs, which is the first important task of the IRMA system. Given an image, we compute posterior probabilities for each image class, as this information is needed for further IRMA processing. Classification is done by using an extended version of Simard’s tangent distance within a kernel density based classifier. We propose a new dis...

A multi-classifier diagnostic system was designed for distinguishing between benign and malignant thyroid nodules from routinely taken (FNA, H&E-stained) cytological images. To construct the multi-classifier system, several combination rules and different mixtures of ensemble classifier members, employing morphological and textural nuclear features, were comparatively evaluated. Experimental re...

In this paper, we investigate how an accurate question classifier contributes to a question answering system. We first present a Maximum Entropy (ME) based question classifier which makes use of head word features and their WordNet hypernyms. We show that our question classifier can achieve the state of the art performance in the standard UIUC question dataset. We then investigate quantitativel...

Recently many researchers concentrate on Multiple Classifier System (MCS) in pattern recognition. Pattern recognition system build in three steps i.e. database, feature extraction and classifier. MCS is Architect by combining more than one classifier i.e. either same or different classifiers for different pattern recognition applications such as emotion recognition, character recognition, face ...

Extended Abstract The classifier system framework is a general-purpose approach to learning and representation designed to exhibit non-brittle behavior in complex, continually varying environments. Broadly speaking, classifier systems are expected to avoid brittle behavior because they implement processes that build and refine models of the environment. One of the most important of these proces...