**Robert A. Dunne ... **288 pages -

**Publisher:** Wiley-Interscience; 1st edition (July 16, 2007) ...

**Language:** English -

**ISBN-10:** 0471741086 -

**ISBN-13:** 978-0471741084 ...

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.