Share
Neural Network Learning: Theoretical Foundations (in English)
Martin Anthony
(Author)
·
Peter L. Bartlett
(Author)
·
Cambridge University Press
· Hardcover
Neural Network Learning: Theoretical Foundations (in English) - Anthony, Martin ; Bartlett, Peter L.
$ 133.32
$ 149.00
You save: $ 15.68
Choose the list to add your product or create one New List
✓ Product added successfully to the Wishlist.
Go to My WishlistsIt will be shipped from our warehouse between
Tuesday, June 18 and
Wednesday, June 19.
You will receive it anywhere in United States between 1 and 3 business days after shipment.
Synopsis "Neural Network Learning: Theoretical Foundations (in English)"
This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. 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. 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. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics.
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
- 0% (0)
All books in our catalog are Original.
The book is written in English.
The binding of this edition is Hardcover.
✓ Producto agregado correctamente al carro, Ir a Pagar.
