A Review of Pattern Recognition and Machine Learning

Abstract

This article aims to provide a concise overview of diverse methodologies employed at different stages of a pattern recognition system, highlighting contemporary research challenges and applications in this dynamic field. The integration of machine learning techniques has played a pivotal role in converging pattern recognition frameworks in academic literature. The process relies heavily on supervised or unsupervised categorization methods to achieve its objectives, with a notable focus on statistical approaches. More recently, there is a growing emphasis on incorporating neural network methodologies and insights from statistical learning theory. Designing an effective recognition system necessitates careful consideration of various factors, including pattern representation, pattern class definition, feature extraction, sensing environment, feature selection, classifier learning and design, cluster analysis, test and training sample selection, and performance assessment.