Advances in kernel methods
Subtitle: support vector learning
By: edited by Bernhard Schölkopf, Christopher J.C. Burges, Alexander J. Smola.
Contributors: Schölkopf, Bernhard.; Burges, Christopher J. C.; Smola, Alexander J.
Publisher: MIT Press; Philomel Books; The MIT Press
Published: 1999
Place of publication: Cambridge, Mass
Language: eng
Number of pages: 376
Subjects: Machine learning.; Algorithms.; Kernel functions.
Description: 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 (p. [353]-371) and index.