Netlab Algorithms for Pattern Recognition

Ian T. Nabney

420 pages, parution le 12/11/2003

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Résumé

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 via the Internet at http://www.ncrg.aston.ac.uk/netlab- 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

Written for:

Final year undergraduate students; postgraduate students; practitioners; academic and industrial researchers

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

Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Ian T. Nabney
Parution	12/11/2003
Nb. de pages	420
Format	15 x 23
Couverture	Broché
Poids	645g
Intérieur	Noir et Blanc
EAN13	9781852334406
ISBN13	978-1-85233-440-6