Different Approaches in Pattern Recognition

Abstract

The objective of this paper is to discuss and compare some aspect of pattern recognition, among the various framework in which pattern recognition has been traditional formulated. The primary goal of pattern recognition is super- vised or unsupervised classification. More recently, neural network techniques and methods imported from statistical learning theory have been receiving increasing attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation.