Introduction to Pattern Recognition and Machine Learning

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.

Contents:

• Introduction
• 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

Readership: Academics and working professionals in computer science.
Key Features:

• The algorithmic approach taken and the practical issues dealt with will aid the reader in writing programs and implementing methods
• Covers recent and advanced topics by providing working exercises, examples and illustrations in each chapter
• Provides the reader with a deeper understanding of the subject matter