The primary objective of this book is to make the mathematical foundations of pattern recognition and computational intelligence easily accessible. It provides comprehensive coverage of various regression methodologies, including Bayesian techniques, Maximum Likelihood, Least Squares, regularization methods, kernel smoothing, and Gaussian processes, alongside Evidence Approximation, Support Vector Regression (SVR), and Relevance Vector Regression (RVR). The text further explores essential dimensionality reduction techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel LDA, and Independent Component Analysis (ICA). Readers will find detailed treatments of probabilistic generative models like Gaussian Mixture Models (GMM) and Hidden Markov Models (HMM), the generalized Expectation-Maximization (EM) algorithm, and probabilistic discriminative models including Support Vector Machines (SVM) and multi-class logistic regression. Additionally, the book covers vital computational intelligence metaheuristicsspecifically Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)and introduces various deep learning algorithms in addition to outlining statistical testing relevant to pattern recognition.

Building upon the core topics of the first edition, this second edition introduces entirely new material on sampling techniques and graphical models. The sampling section covers rejection sampling, Gibbs sampling, and importance sampling. The newly added section on graphical models discusses computing marginal probabilities via the sum-product algorithm and details methods for identifying optimal random variable values that maximize joint probability. Finally, the chapters on linear regression and probabilistic generative models have been thoroughly revised and updated for this edition.

Presents an update on pattern recognition and the computational intelligence using Matlab Includes mixtures of theory, math, and algorithms, letting readers understand the concepts quickly Outlines classifiers, regression models, and statistical tests for pattern recognition using computational intelligence

Autorentext

Dr. E. S. Gopi is a professor in the Department of Electronics and Communication Engineering at the National Institute of Technology, Tiruchirappalli (Government of India), with 25 years of experience in teaching and research. He has authored eight books and edited three publications with Springer, primarily focusing on signal processing and pattern recognition. He has contributed to several research publications, including journal articles, book chapters, and conference proceedings. Beyond writing, he creates instructional videos on topics such as pattern recognition, statistical theory of communication, and linear algebra, which are popular among students. He actively engages with the IEEE community as the Workshop, Tutorials & Symposia Officer for the Machine Learning for Communications Emerging Technologies Initiative. He was recognized as a Scholar GPS 2025 Highly Ranked Scholar - Lifetime in the specialty of Digital Signal. His research interests include machine intelligence, pattern recognition, statistical signal processing, and computational intelligence.

Inhalt

Introduction.- Dimensionality Reduction Techniques.- Linear Classifier Techniques.- Regression Techniques.- Probabilistic Supervised Classifier and Unsupervised Clustering.- Computational Intelligence.- Statistical Test in Pattern Recognition.- Conclusion.