A Theory of Learning and Generalization (Electronic Workshops in Computing) [Hardcover]

M. Vidyasagar (Author)

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Learning and Generalization: With Applications to Neural Networks
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Book Description

ISBN-10: 3540761209 | ISBN-13: 978-3540761204 | Publication Date: January 15, 1997

Provides a formal mathematical theory for addressing intuitive questions of the type: How does a machine learn a new concept on the basis of examples? How can a neural network, after sufficient training, correctly predict the output of a previously unseen input? How much training is required to achieve a specified 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 interval of time? This text treats the problem of machine learning in conjunction with the theory of empirical processes, the latter being a well-established branch of probability theory. The treatment of both topics side by side leads to new insights, as well as new results in both topics.

#outer_postBodyPS { display: none; } #psGradient { display: none; } #psPlaceHolder { display: none; } #psExpand { display: none; } Provides a formal mathematical theory for addressing intuitive questions of the type: How does a machine learn a new concept on the basis of examples? How can a neural network, after sufficient training, correctly predict the output of a previously unseen input? How much training is required to achieve a specified 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 interval of time? This text treats the problem of machine learning in conjunction with the theory of empirical processes, the latter being a well-established branch of probability theory. The treatment of both topics side by side leads to new insights, as well as new results in both topics.

Product Details

Hardcover: 383 pages
Publisher: Springer-Verlag Telos (January 15, 1997)
Language: English
ISBN-10: 3540761209
ISBN-13: 978-3540761204
Product Dimensions: 9.8 x 6.5 x 1.2 inches
Shipping Weight: 1.6 pounds
Amazon Best Sellers Rank: #400,546 in Books (See Top 100 in Books)
- #39 in Books > Computers & Technology > Computer Science > Artificial Intelligence > Computer Mathematics
- #48 in Books > Computers & Technology > Computer Science > Artificial Intelligence > Machine Learning
- #62 in Books > Computers & Technology > Computer Science > Artificial Intelligence > Neural Networks

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