new book, Feedforward Neural Network Methodology

[Terrence Fine]

A new monograph, Feedforward Neural Network Methodology, by Terrence L. Fine,
has appeared in the Springer Series on Statistics for Engineering and
Information Science. It is priced at $69.95 for 340 pages.

This monograph  provides a thorough and coherent introduction to
the mathematical properties of feedforward neural networks and to the
computationally intensive methodology that has enabled their successful
application to complex problems of pattern classification, forecasting, 
regression, and nonlinear systems  modeling. 

Coherence is achieved by focusing on the class of
feedforward neural networks, also called multilayer perceptrons,
and orienting the discussion around the four questions:

What functions can the network architecture implement
or closely approximate?

What is the complexity of an achievable implementation?

Given the resources dictated by the complexity considerations,
how do you select a network to achieve the task?

How well will the selected network learn or generalize to new
problem instances?

Table of Contents
Preface
Chapter 1: Background and Organization
Chapter 2: Perceptrons---Networks with a Single Node
Chapter 3: Feedforward Networks I: Generalities and LTU Nodes
Chapter 4: Feedforward Networks II: Real-valued Nodes
Chapter 5: Algorithms for Designing Feedforward Networks
Chapter 6: Architecture Selection and Penalty Terms
Chapter 7: Generalization and Learning
Appendix: A Note on Use as a Text
References
Index