Textbook on Pattern Recognition

Presents both classical and current theoretical foundation of pattern recognition and practice of supervised, unsupervised and reinforces learning to build a complete background of professionals and engineering students.

Every pattern of the image and design cycles has been discussed in detail through illustrative examples and applications. The very latest method are incorporated in this text book including k-mean clustering, k-nearest neighborhood methods etc.

• Coverage of Learning Adaptation.
• Design Cycle of Pattern Recognition.
• Algorithms have been presented in step by step in illustration of the same in problems.
• Probability distribution function with Cumulative and Uniform PDF methods
• Coverage of Optical Character recognition, face recognition, and speech recognition and image analysis.

The information is clearly presented and illustrated by many examples and applications and is designed for use as a text for course in pattern recognition for both undergraduate and post graduate engineering students.

Table of Contents

• Preface
• PATTERN RECOGNITION: Introduction
• Basics of Pattern Recognition
• Pattern Recognition Applications
• Pattern Recognition Structure
• Design Principles of Pattern Recognition
• The Design Cycle of Pattern Recognition
• Learning
• Adaptation
• Basic Pattern Recognition Approaches
• MATHEMATICAL FOUNDATIONS: Introduction to Linear Algebra
• Probability Theory
• Important Properties of Probabilities
• Estimation
• Mean
• Variance
• Probability Distribution Function
• Classification of Probability Distribution Function
• Chi-Square Test
• STATISTICAL PATTERN RECOGNITION: Introduction
• Bayes Decision Theory
• PARAMETER ESTIMATION METHODS: Introduction
• Maximum Likelihood Estimation
• Bayesian Estimation
• Dimension Reduction
• Principal Components Analysis
• Computing Process of Principal Components
• Fisher Linear Discriminate Analysis
• EXPECTATION MAXIMIZATION: Introduction
• Hidden Markov Model
• Gaussian Mixture Model
• NON-PARAMETRIC TECHNIQUES OF ESTIMATION: Density Estimation
• K-Nearest Neighbor Rule
• Nearest Neighbor Rules
• UNSUPERVISED LEARNING AND CLUSTERING: Introduction
• Clustering
• Clustering Techniques
• Linkage Method of Hierarchical Clustering
• K-Mean Clustering
• Cluster Validation
• Index.

Anuj Bhardwaj / Parag Verma Department of Computer Science and Engineering Gyan Bharati Institute of Technology, Meerut