Klicka på en bild för att gå till Google Book Search.
Laddar... Learning Kernel Classifiers: Theory and Algorithmsav Ralf Herbrich
Ingen/inga Laddar...
Gå med i LibraryThing för att få reda på om du skulle tycka om den här boken. Det finns inga diskussioner på LibraryThing om den här boken. It's now available as an ebook on the MIT press portal http://mitpress-ebooks.mit.edu/product/learning-kernel-classifiers inga recensioner | lägg till en recension
Ingår i serien
Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library. Inga biblioteksbeskrivningar kunde hittas. |
Pågående diskussionerIngen/inga
Google Books — Laddar... GenrerMelvil Decimal System (DDC)006.3Information Computing and Information Special Topics Artificial IntelligenceKlassifikation enligt LCBetygMedelbetyg: Inga betyg.Är det här du? |