Murty M.N., Devi D.V.S. Introduction to Pattern Recognition and Machine Learning

IISc Press/World Scientific, 2015. — 402 p.Pattern recognition (PR) is a classical area and some of the important topics covered in the books on PR include representation of patterns, classification, and clustering. There are different paradigms for pattern recognition including the statistical and structural paradigms. The structural or linguistic paradigm has been studied in the early days using formal language tools. Logic and automata have been used in this context. In linguistic PR, patterns could be represented as sentences in a logic; here, each pattern is represented using a set of primitives or sub-patterns and a set of operators. Further, a class of patterns is viewed as being generated using a grammar; in other words, a grammar is used to generate a collection of sentences or strings where each string corresponds to a pattern. So, the classification model is learnt using some grammatical inference procedure; the collection of sentences corresponding to the patterns in the class are used to learn the grammar. A major problem with the linguistic approach is that it is suited to dealing with structured patterns and the models learnt cannot tolerate noise.
On the contrary the statistical paradigm has gained a lot of momentum in the past three to four decades. Here, patterns are viewed as vectors in a multi-dimensional space and some of the optimal classifiers are based on Bayes rule. Vectors corresponding to patterns in a class are viewed as being generated by the underlying probability density function; Bayes rule helps in converting the prior probabilities of the classes into posterior probabilities using the likelihood values corresponding to the patterns given in each class. So, estimation schemes are used to obtain the probability density function of a class using the vectors corresponding to patterns in the class. There are several other classifiers that work with vector representation of patterns. We deal with statistical pattern recognition in this book.
This book deals with the material at an early graduate level. Beginners are encouraged to read our introductory book Pattern recognition: An Algorithmic Approach published by Springer in 2011 before reading this book (/file/2125200/).Types of Data
Feature Extraction and Feature Selection
Bayesian Learning
Classification
Classification using Soft Computing Techniques
Data Clustering
Soft Clustering
Application — Social and Information Networks