Neural Network Learning: Theoretical Foundations
| Author | : | |
| Rating | : | 4.79 (685 Votes) |
| Asin | : | 052157353X |
| Format Type | : | paperback |
| Number of Pages | : | 404 Pages |
| Publish Date | : | 2013-07-22 |
| Language | : | English |
DESCRIPTION:
Amazing! Awesome! Staggering! A Customer A stonking blockbuster of a book, filled with raw power and suspense! From the heart stopping narrative on the on the Need for Conditions on the Activation Functions, to the torrid account of Classes of Finite Pseudo-dimension, this is truly the most exhilarating and disturbing description yet of
Each chapter has a bibliographical section with helpful suggestions for further readingthis book would be best utilized within an advanced seminar context where the student would be assisted with examples, exercises, and elaborative comments provided by the professor." Telegraphic Reviews . "This book gives a thorough but nevertheless self-contained treatment of neural network learning from the perspective of computational learning theory." Mathematical Reviews"This book is a rigorous treatise on neural networks that is written for advanced graduate students i
It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. This important work describes recent theoretical advances in the study of artificial neural networks. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. The book is self-co