Könyv Neural Network Learning Martin AnthonyPeter L. Bartlett

Neural Network Learning

Theoretical Foundations

Nyelv: Angol
Kötés: Kemény kötésű
Elérhetőség: Beszállítói készleten
Küldés 9-15 napon belül
61 258 Ft
First published in 1999, this book describes theoretical advances in the study of artificial neural...

Információk a könyvről

Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
1999
oldal
404
EAN
9780521573535
ISBN
052157353X
Enbook ID
02035762
Súly
666
Méretek
234 x 158 x 26

Teljes leírás

First published in 1999, this book describes 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. Research on pattern classification with binary-output networks is surveyed, including a discussion of the relevance of the Vapnik-Chervonenkis dimension, and calculating estimates of the dimension for several neural network models. A model of classification by real-output networks is developed, and the usefulness of classification with a 'large margin' is demonstrated. The authors explain the role of scale-sensitive versions of the Vapnik-Chervonenkis dimension in large margin classification, and in real prediction. They 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 is intended to be accessible to researchers and graduate students in computer science, engineering, and mathematics.

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