This volume provides students, researchers, and application developers with the knowledge and tools to get the most out of using neural networks and related data modelling techniques to solve pattern recognition problems. Each chapter covers a group of related pattern recognition techniques and includes a range of examples to show how these techniques can be applied to solve practical problems.
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Index Terms
- NETLAB: algorithms for pattern recognition
Reviews
Mihai Caramihai
There is an increasing need for engineers and specialists to develop current and next-generation intelligent technologies. To be successful, these engineers and specialists will need to draw on skills in many areas, such as data modeling, neural nets, pattern recognition, and visualization methods. Many current applications in neural computing are not successful because they are carried out in an ad hoc fashion; most introductory texts do not approach the subject within a pattern recognition framework, and the software does not support a dedicated development approach. This book aims to provide students and practitioners with the knowledge and tools to get the most out of neural networks and related models. The book has ten chapters, and covers a wide range of issues, topics, and paradigms that go beyond the traditional scope of pattern recognition using neural nets. The focus of the book is a group of related pattern recognition techniques, including a range of examples to show how these techniques can be applied to solve practical problems. The first chapter provides an introduction to the software tools used in the book. A brief tour of MATLAB is included, with a particular focus on efficient programming methods. Chapter 2 covers multivariate optimization algorithms; these are implemented as general-purpose routines that can be used for a wide variety of applications. In chapter 3, there is a detailed treatment of one of the most important methods for doing Gaussian mixture models. In chapter 4 the simplest form of predictive data models is introduced (specifically, the generalized linear model). A comprehensive treatment of the multi-layer perceptron is given in chapter 5, and the radial basis function network is treated in chapter 6. Chapter 7 covers a particular strength of NETLAB: the repertoire of visualization methods. The last three chapters are concerned with the Bayesian perspective on data modeling and inference. A Web site accompanies the book (http://www.ncrg.aston.ac.uk/netlab). This site provides a unique collection of many of the most important pattern recognition algorithms, realized in MATLAB scripts. The author is senior researcher with a background in teaching, research, and consulting in England. He brings to this work considerable experience in application development using techniques in pattern recognition. As a result, the book is useful to students and practitioners in computer science and related disciplines studying pattern recognition; they will learn the knowledge, skills, and techniques for designing and evaluating state-of-the-art products in the field. Researchers and developers will find stimulating ideas regarding the most important pattern recognition application solutions. Online Computing Reviews Service
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