Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning)

Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning)
Автор
 
Год
 
Страниц
 
0
ISBN
 
026208306X
Издатель
 
Ronn King Consulting
Категория
 
Обучение машины
Искать в интернет библиотекахКупить

Описание:

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...

Похожие книги

Maxwell?s EquationsMaxwell?s Equations
Автор: Paul G. Huray
Год: 2009
Probability and Random ProcessesProbability and Random Processes
Автор: Venkatarama Krishnan
Год: 2006
Probability and Random ProcessesProbability and Random Processes
Автор: Venkatarama Krishnan
Год: 2005