Statistical Learning Theory
Statistical Learning Theory
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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 |