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. Features of particular interest include:

• A NETLAB toolbox which is freely available
• Worked examples, demonstration programs and over 100 graded exercises
• Cutting edge research made accessible for the first time in a highly usable form
• Comprehensive coverage of visualisation methods, Bayesian techniques for neural networks and Gaussian Processes

Although primarily a textbook for teaching undergraduate and postgraduate courses in pattern recognition and neural networks, this book will also be of interest to practitioners and researchers who can use the toolbox to develop application solutions and new models.

"...provides a unique collection of many of the most important pattern recognition algorithms. With its use of compact and easily modified MATLAB scripts, the book is ideally suited to both teaching and research."
Christopher Bishop, Microsoft Research, Cambridge, UK

"...a welcome addition to the literature on neural networks and how to train and use them to solve many of the statistical problems that occur in data analysis and data mining" Jack Cowan, Mathematics Department, University of Chicago, US

"If you have a pattern recognition problem, you should consider NETLAB; if you use NETLAB you must have this book." Keith Worden, University of Sheffield, UK

Three chapters cover new material not available in any other textbook Comes with NETLAB software (suitable for Windows, Mac and UNIX environments)

Inhalt
Introduction.- Parameter Optimisation Algorithms.- Density Modelling and Clustering.- Single-Layer Networks.- The Multi-Layer Perceptron.- Radial Basis Functions.- Visualisation and Latent Variable Models.- Sampling.- Bayesian Techniques.- Gaussian Processes.- Linear Algebra and Matrices.- Algorithm Error Analysis.- Function Index.- Subject Index.