Titre : | Advances in Kernel Methods : Support vector learning | Type de document : | texte imprimé | Auteurs : | Bernhard SCHÖLKOPF, Editeur scientifique ; Christopher J.C. BURGES, Editeur scientifique ; Alexander J. SMOLA, Editeur scientifique | Editeur : | Cambridge, Massachusetts [U.S.A.] : The M.I.T. Press | Année de publication : | Cop. 1999 | Importance : | VII-376 p. | ISBN/ISSN/EAN : | 978-0-262-19416-7 | Langues : | Anglais | Mots-clés : | méthode de Kernel algorithme fonction de Kernel | Résumé : | 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. | Note de contenu : | index, références |
Advances in Kernel Methods : Support vector learning [texte imprimé] / Bernhard SCHÖLKOPF, Editeur scientifique ; Christopher J.C. BURGES, Editeur scientifique ; Alexander J. SMOLA, Editeur scientifique . - Cambridge, Massachusetts (U.S.A.) : The M.I.T. Press, Cop. 1999 . - VII-376 p. ISBN : 978-0-262-19416-7 Langues : Anglais Mots-clés : | méthode de Kernel algorithme fonction de Kernel | Résumé : | 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. | Note de contenu : | index, références |
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