An edition of Advances in kernel methods
(1998)
Advances in Kernel Methods
Support Vector Learning
by Alexander J. Smola
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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.
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Subjects
Algorithms, Kernel functions, Machine learning, Vector analysis, Apprentissage automatique, Algorithmes, Noyaux (Mathématiques), COMPUTERS, Enterprise Applications, Business Intelligence Tools, Intelligence (AI) & Semantics, Kunstmatige intelligentie, Algoritmen, Patroonherkenning, Functies (wiskunde), Machine-learning, Fiction, Chinese Americans, Juvenile fiction, Brothers, Railroads, Central Pacific Railroad Company| Edition | Availability |
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1
Advances in kernel methods: support vector learning
1999, MIT Press, Philomel Books, The MIT Press
in English
0262194163 9780262194167
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2
Advances in Kernel Methods: Support Vector Learning
1999, MIT Press
in English
0585128294 9780585128290
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3
Advances in Kernel Methods: Support Vector Learning
December 18, 1998, The MIT Press
Hardcover
in English
0262194163 9780262194167
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Book Details
First Sentence
"Let us start with the problem of learning how to recognize patterns."
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| July 15, 2024 | Edited by MARC Bot | import existing book |
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