Authors:
- Comprehensive; this book covers all aspects of learning theory and its applications. Other books have a narrower focus
- It contains applications not only to neural networks but also to control systems
- The author has recently been selected to receive the Hendrik W. Bode Lecture Prize awarded by the IEEE Control Systems Society
- Includes supplementary material: sn.pub/extras
Part of the book series: Communications and Control Engineering (CCE)
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Table of contents (12 chapters)
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Front Matter
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Back Matter
About this book
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.
Authors and Affiliations
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Tata Consultancy Services, Secunderabad, India
M. Vidyasagar
Bibliographic Information
Book Title: Learning and Generalisation
Book Subtitle: With Applications to Neural Networks
Authors: M. Vidyasagar
Series Title: Communications and Control Engineering
DOI: https://doi.org/10.1007/978-1-4471-3748-1
Publisher: Springer London
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eBook Packages: Springer Book Archive
Copyright Information: Springer-Verlag London 2003
Hardcover ISBN: 978-1-85233-373-7Published: 27 September 2002
Softcover ISBN: 978-1-84996-867-6Published: 19 October 2010
eBook ISBN: 978-1-4471-3748-1Published: 14 March 2013
Series ISSN: 0178-5354
Series E-ISSN: 2197-7119
Edition Number: 2
Number of Pages: XXI, 488
Additional Information: Originally published with the title: A Theory of Learning and Generalization
Topics: Electrical Engineering, Control and Systems Theory, Systems Theory, Control, Probability Theory and Stochastic Processes, Group Theory and Generalizations, Computer Communication Networks