Overview

Learning and Generalization provides a formal mathematical theory addressing intuitive questions of the type: • How does a machine learn a concept on the basis of examples? • How can a neural network, after training, correctly predict the outcome of a previously unseen input? • How much training is required to achieve a given level of accuracy in the prediction? • How can one identify the dynamical behaviour of a nonlinear control system by observing its input-output behaviour over a finite time? The second edition covers new areas including: • support vector machines; • fat-shattering dimensions and applications to neural network learning; • learning with dependent samples generated by a beta-mixing process; • connections between system identification and learning theory; • probabilistic solution of 'intractable problems' in robust control and matrix theory using randomized algorithms. It also contains solutions to some of the open problems posed in the first edition, while adding new open problems.

Full Product Details

Author:   Mathukumalli Vidyasagar
Publisher:   Springer London Ltd
Imprint:   Springer London Ltd
Edition:   Softcover reprint of hardcover 2nd ed. 2002
Dimensions:   Width: 15.50cm , Height: 2.60cm , Length: 23.50cm
Weight:   0.783kg
ISBN:  

9781849968676

ISBN 10:   1849968675
Pages:   488
Publication Date:   19 October 2010
Audience:   Professional and scholarly ,  Professional and scholarly ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
We will order this item for you from a manufactured on demand supplier.

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