INTRODUCTION TO PATTERN RECOGNITION BAYESIAN DECISION THEORY MAXIMUM-LIKELIHOOD AND BAYESIAN PARAMETER ESTIMATION NONPARAMETRIC TECHNIQUES LINEAR DISCRIMINANT FUNCTIONS ARTIFICIAL NEURAL NETWORKS NONMETRIC METHODS ALGORITHM-INDEPENDENT MACHINE LEARNING UNSUPERVISED LEARNING AND CLUSTERING

Pattern Recognition is a classic reference in the field which has been an invaluable resource preferred by students, academics, researchers, and other interested readers for more than four decades. Starting with the introductory concepts of pattern classification, the book lays the theoretical foundations of Bayesian decision theory and then focuses on key topics such as parameter estimation, discriminant analysis, neural networks, and nonmetric methods. It finally covers machine learning, unsupervised learning, and different clustering techniques. The book incorporates a host of pedagogical features, including worked examples, extensive graphics, expanded exercises, and computer project topics