Learning Kernel Classifiers
- Theory and Algorithms
- Indbinding:
- Paperback
- Sideantal:
- 384
- Udgivet:
- 1. november 2022
- Størrelse:
- 178x229x21 mm.
- Vægt:
- 608 g.
- 2-3 uger.
- 16. december 2024
På lager
Forlænget returret til d. 31. januar 2025
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- Adgang til 70.000+ titler
- Ingen binding
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Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Learning Kernel Classifiers
An overview of the theory and application of kernel classification methods. 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.
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