The book is solely dedicated to explaining the theoretical background of a group of functions inside a MATLAB toolbox called NETLAB. The toolbox is available for free and contains 175 different functions in the form of .mat files for the simulation of neural networks and pattern analysis. Each function has its own .htm HELP file. The functions, as well as the book, are divided in parameter optimization algorithms, density clustering and clustering, single layer networks, multiple layer perceptron, radial basis functions, latent variable models, sampling, Bayesian techniques and Gaussian processes. The execution of the functions as well as their results was not checked as part of the book review.

The book 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. A few examples are included to support the explanation of how to use the functions.

Distribution of the material seems ad hoc due to the variety of functions and their purpose. All notations in the formulas appear uniformly presented. Examples are short but self-explicative. Exercises are included which are not always dependent on the software. And an excellent list of references is included at the end of the book.

The book is the technical manual of the NETLAB software, 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.

Click here for Top of Page
Right Arrow: Next
Right Arrow: Previous




Algorithms for

Pattern Recognition


by Ian T. Nabney

Springer, 2001

Series:  Advances in Computer Vision and Pattern Recognition


Reviewed by

Eleazar Jimenez Serrano (Japan)

Click on the image (above) to go to the publisher’s web page for this book where you will find a description of the book,  the Table of Contents, and a link  to Supporting Online Material