| 刊年 | 2002 |
| G/SMD | リモートファイル |
| 形態 | 1 online resource (xviii, 626 p.) : ill. |
| シリーズ名 | Adaptive computation and machine learning series
|
| 注記 | Includes bibliographical references (p. [591]-616) and index
Restricted to subscribers or individual electronic text purchasers
In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: the Support Vector Machine (SVM). This gave rise to a new class of theoretically elegant learning machines that use a central concept of SVMs -- -kernels--for a number of learning tasks. Kernel machines provide a modular framework that can be adapted to different tasks and domains by the choice of the kernel function and the base algorithm. They are replacing neural networks in a variety of fields, including engineering, information retrieval, and bioinformatics.Learning with Kernels provides an introduction to SVMs and related kernel methods. Although the book begins with the basics, it also includes the latest research. It provides all of the concepts necessary to enable a reader equipped with some basic mathematical knowledge to enter the world of machine learning using theoretically well-founded yet easy-to-use kernel algorithms and to understand and apply the powerful algorithms that have been developed over the last few years
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=6267332(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 | Schölkopf, Bernhard
Smola, Alexander J.
|
| ISBN | 9780262256933(: electronic bk)
|
| 無効/取消ISBN | 9780262194754(: electronic bk)
|
| 件名 | LCSH:Machinelearning
LCSH:Algorithms
LCSH:Kernelfunctions
|
| NCID | 6267332 |
| IDENT | https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267332 |