| 刊年 | 2000 |
| G/SMD | リモートファイル |
| 形態 | 1 online resource (vi, 412 p.) : ill. |
| シリーズ名 | Advances in neural information processing systems [i.e. Neural information processing series]
|
| 注記 | Includes bibliographical references (p. [389]-407) and index
Restricted to subscribers or individual electronic text purchasers
The concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms.The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba
Also available in print
Mode of access: World Wide Web
Description based on PDF viewed 12/23/2015
URL:https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267437(Abstract with links to resource) |
| 出版国 | アメリカ合衆国 |
| 標題言語 | 英語 |
| 本文言語 | 英語 |
| 著者情報 | Smola, Alexander J
|
| ISBN | 9780262283977(: electronic bk)
|
| 無効/取消ISBN | 9780262194488(: electronic bk)
|
| 件名 | LCSH:Kernelfunctions
LCSH:Algorithms
LCSH:Machinelearning
|
| NCID | 6267437 |
| IDENT | https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267437 |