| 书目名称 | The Nature of Statistical Learning Theory |
| 编辑 | Vladimir N. Vapnik |
| 视频video | |
| 概述 | The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization..It considers learning as a general problem of function estimation based |
| 丛书名称 | Information Science and Statistics |
| 图书封面 |  |
| 描述 | The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It considers learning as a general problem of function estimation based on empirical data. Omitting proofs and technical details, the author concentrates on discussing the main results of learning theory and their connections to fundamental problems in statistics. These include: * the setting of learning problems based on the model of minimizing the risk functional from empirical data * a comprehensive analysis of the empirical risk minimization principle including necessary and sufficient conditions for its consistency * non-asymptotic bounds for the risk achieved using the empirical risk minimization principle * principles for controlling the generalization ability of learning machines using small sample sizes based on these bounds * the Support Vector methods that control the generalization ability when estimating function using small sample size. The second edition of the book contains three new chapters devoted to further development of the learning theory and SVM techniques. These include: * the theory of direct method of learning based on solving m |
| 出版日期 | Book 2000Latest edition |
| 关键词 | Conditional probability; Statistical Learning; Statistical Theory; cognition; control; learning; pattern r |
| 版次 | 2 |
| doi | https://doi.org/10.1007/978-1-4757-3264-1 |
| isbn_softcover | 978-1-4419-3160-3 |
| isbn_ebook | 978-1-4757-3264-1Series ISSN 1613-9011 Series E-ISSN 2197-4128 |
| issn_series | 1613-9011 |
| copyright | Springer Science+Business Media New York 2000 |
发表于 2025-3-21 19:52:28
