Statistical Learning Theory
Statistical Learning Theory
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Summary
This book is devoted to the statistical theory of learning and generalization, that is, the problem of choosing the desired function on the basis of empirical data. The author will present the whole picture of learning and generalization theory. Learning theory has applications in many fields, such as psychology, education and computer science.
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Statistical Learning Theory by Vladimir Vapnik
A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the basis of empirical data. Highly applicable to a variety of computer science and robotics fields, this book offers lucid coverage of the theory as a whole. Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.Vladimir Naumovich Vapnik is one of the main developers of the Vapnik-Chervonenkis theory of statistical learning, and the co-inventor of the support vector machine method, and support vector clustering algorithm.
| SKU | Unavailable |
| ISBN 13 | 9780471030034 |
| ISBN 10 | 0471030031 |
| Title | Statistical Learning Theory |
| Author | Vladimir Vapnik |
| Series | Adaptive And Cognitive Dynamic Systems: Signal Processing Learning Communications And Control |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Wiley-Interscience |
| Year published | 1998-10-12 |
| Number of pages | 768 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |