Off-line handwritten numeral recognition is a very difficult task. It is hard to achieve high recognition results using a single set of features and a single classifier, since handwritten numerals contain many pattern variations which mostly depend upon individual writing styles. In this paper, we propose a recognition system using hybrid features and a combined classifier. To improve recogniti...

One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. This paper addresses using an ensemble of classification methods for recognizing totally unconstrained handwritten numerals. Due to a great variety of individual writing styles, the problem is very diffic...

The support vector machine classifier is a linear maximum margin classifier. It performs very well in many classification applications. Although, it could be extended to nonlinear cases by exploiting the idea of kernel, it might still suffer from the heterogeneity in the training examples. Since there are very few theories in the literature to guide us on how to choose kernel functions, the sel...

A method for pattern classification using genetic algorithms (GAs) has been recently described in Pal, Bandyopadhyay and Murthy (1998), where the class boundaries of a data set are approximated by a fixed number H of hyperplanes. As a consequence of fixing H a priori, the classifier suffered from the limitation of overfitting (or underfitting) the training data with an associated loss of its ge...

In the field of pattern recognition, multiple classifier systems based on the combination of outputs of a set of different classifiers have been proposed as a method for the development of high performance classification systems. In this chapter, the problem of multiple classifier system design is discussed and the reader is provided with a critical survey of the state of the art. A formulation...

This paper presents a multi-classifier system design controlled by the topology of the learning data. Our work also introduces a training algorithm for an incremental self-organizing map (SOM). This SOM is used to distribute classification tasks to a set of classifiers. Thus, the useful classifiers are activated when new data arrives. Comparative results are given for synthetic problems, for an...