Fundamentals of Pattern Recognition and Machine Learning is designed for a one or two-semester introductory course in Pattern Recognition or Machine Learning at

This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to distributiona

This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of proble

A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theor

This book is the first of its kind to discuss error estimation with a model-based approach. From the basics of classifiers and error estimators to distributiona