A Statistical Approach to Neural Networks for Pattern Recognition / Wiley Series in Computational Statistics (PDF)

An accessible and up-to-date treatment featuring the connection
between neural networks and statistics

A Statistical Approach to Neural Networks for Pattern
Recognition presents a statistical treatment of the Multilayer
Perceptron (MLP), which is the most widely used of the neural
network models. This book aims to answer questions that arise when
statisticians are first confronted with this type of model, such
as:

How robust is the model to outliers?

Could the model be made more robust?

Which points will have a high leverage?

What are good starting values for the fitting algorithm?

Thorough answers to these questions and many more are included,
as well as worked examples and selected problems for the reader.
Discussions on the use of MLP models with spatial and spectral data
are also included. Further treatment of highly important principal
aspects of the MLP are provided, such as the robustness of the
model in the event of outlying or atypical data; the influence and
sensitivity curves of the MLP; why the MLP is a fairly robust
model; and modifications to make the MLP more robust. The author
also provides clarification of several misconceptions that are
prevalent in existing neural network literature.

Throughout the book, the MLP model is extended in several
directions to show that a statistical modeling approach can make
valuable contributions, and further exploration for fitting MLP
models is made possible via the R and S-PLUS® codes that are
available on the book's related Web site. A Statistical Approach to
Neural Networks for Pattern Recognition successfully connects
logistic regression and linear discriminant analysis, thus making
it a critical reference and self-study guide for students and
professionals alike in the fields of mathematics, statistics,
computer science, and electrical engineering.