Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning)
Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. Get and download textbook Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning) for free
Learning Kernel Classifiers Theory and Algorithms, ISBN-13: 9780262083065, ISBN-10: 026208306X
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. Th Learning Kernel Classifiers new edition
Download free books for Learning kernel classifiers. Theory and algorithms
Learning kernel classifiers. Theory and algorithms: Ralf Herbrich
Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning); ISBN: 026208306X; Condition: Used; Like New
Store Search search Title, ISBN and Author Learning Kernel Classifiers: Theory and Algorithms by Ralf Herbrich Estimated delivery 3-12 business days Format Hardcover Condition Brand New Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier mdash;a limited, but well-established and comprehensively studied model mdash;and extends its applicability to a wide range of nonlinear pattern-recognition tas
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:
Learning Kernel Classifiers Textbook
Th
