Introduction to Pattern Recognition and Machine Learning, Volume 5

This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current topics — neural networks, support vector machines and decision trees — attributed to the recent vast progress in this field are also dealt with. Introduction to Pattern Recognition and Machine Learning will equip readers, especially senior computer science undergraduates, with a deeper understanding of the subject matter.

Readership: Academics and working professionals in computer science.

Table of Contents

Chapter 1. Introduction
Chapter 2. Types of Data
Chapter 3. Feature Extraction and Feature Selection
Chapter 4. Bayesian Learning
Chapter 5. Classification
Chapter 6. Classification using Soft Computing Techniques
Chapter 7. Data Clustering
Chapter 8. Soft Clustering
Chapter 9. Application — Social and Information Networks