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

Format:

Book

Contributor:

Schölkopf, Bernhard.

Burges, Christopher J. C.

Smola, Alexander J.

Language:

English

Subjects (All):

Machine learning.

Algorithms.

Kernel functions.

Physical Description:

vii, 376 pages : illustrations ; 26 cm

Place of Publication:

Cambridge, Mass. : MIT Press, [1999]

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.

ISBN:

0262194163

OCLC:

39706952