NETLAB: Algorithms for Pattern Recognition - Brossura

T. Nabney, Ian

Brossura

ISBN 10: 1852334401 ISBN 13: 9781852334406

Casa editrice: Springer, 2004

Riassunto

Getting the most out of neural networks and related data modelling techniques is the purpose of this book. The text, with the accompanying Netlab toolbox, provides all the necessary tools and knowledge. Throughout, the emphasis is on methods that are relevant to the practical application of neural networks to pattern analysis problems. All parts of the toolbox interact in a coherent way, and implementations and descriptions of standard statistical techniques are provided so that they can be used as benchmarks against which more sophisticated algorithms can be evaluated. Plenty of examples and demonstration programs illustrate the theory and help the reader understand the algorithms and how to apply them.

Su questo libro

Recensione

From the reviews:

"...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

"Breezing through the elementary algorithms, Nabney takes readers on a tour of the more sophisticated approaches used by real practitioners. ... It is an invaluable resource for the serious student of neural networks."
David S. Touretzky, Computer Science Department, Carnegie Mellon University, US

"Anyone who intends to use Matlab for pattern recognition and related neural computing applications will benefit from this book. It provides a valuable insight into the methods used within the NETLAB toolbox and serves as a useful reference."
Steve King, Strategic Research Centre, Rolls-Royce plc., UK

"The book aims to provide readers with the knowledge and tools to get the most out of neural networks ... . A series of worked examples and illustrative demonstration programs are also supplied helping the reader to understand the algorithms ... . The book provides an excellent collection of the most important algorithms in pattern recognition. The book can be used as a textbook for teaching undergraduate and postgraduate courses in pattern recognition but it also proves extremely worthy to practitioners and researchers ... ." (Luminita State, Zentralblatt MATH, Vol. 1011, 2003)

"In this book, Ian Nabney provides a well-organized description of NETLAB along with plenty of demonstration programs, worked examples and exercises. ... Throughout the book, the author strictly follows a uniform style in each chapter. ... I think the unification of the style can increase the readability of the book. ... The most benefited readers may be the researchers in the same area. Through this book, they can easily find the algorithms they are interested in and use the algorithms for their own purpose." (Lu Zhang, Expert Update, Vol. 5 (3), 2002)

“The book is solely dedicated to explaining the theoretical background of ... NETLAB. ... mainly presents the algorithms of the functions, their MATLAB program, the formulas used and the required inputs from the users in order to use the functions. ... an excellent list of references is included at the end of the book. ... useful as an illustrative reference when used with the actual software, but also containing enough scientific explanations and theory to be considered a good reference book alone.” (Eleazar Jimenez Serrano, IAPR Newsletter, January, 2011)

Contenuti

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.

Dettagli dell?edizione

• Editore: Springer
• Data di pubblicazione: 2004
• ISBN 10: 1852334401
• ISBN 13: 9781852334406
• Rilegatura: Copertina flessibile
• Lingua: Inglese
• Numero di pagine: 440
• Contatto del produttore: non disponibile