Advances in kernel methods : support vector learning / edited by Bernhard Schölkopf, Christopher J.C. Burges, Alexander J. Smola.

Format:

Book

Contributor:

Burges, Christopher J. C., editor.

Schölkopf, Bernhard, editor.

Smola, Alexander J., editor.

Language:

English

Subjects (All):

Algorithms.

Kernel functions.

Machine learning.

Physical Description:

1 online resource (vii, 376 pages) : illustrations

Other Title:

MIT Press CogNet.

Place of Publication:

Cambridge, Massachusetts : The MIT Press, [1999]

System Details:

text file

Summary:

The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university researchers and engineers developing applications for the corporate world, form a Who's Who of this exciting new area.

Notes:

Includes bibliographical references (pages 353-371) and index.

Description based on print version record.

Other Format:

Print version: Advances in kernel methods.

ISBN:

9780262283199

0262283190

Access Restriction:

Restricted for use by site license.