Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning)
| Author | : | |
| Rating | : | 4.37 (713 Votes) |
| Asin | : | 026208306X |
| Format Type | : | paperback |
| Number of Pages | : | 384 Pages |
| Publish Date | : | 2015-04-13 |
| Language | : | English |
DESCRIPTION:
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. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. 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
Ralf Herbrich is a Postdoctoral Researcher in the Machine Learning and Perception Group at Microsoft Research Cambridge and a Research Fellow of Darwin College, University of Cambridge.
About the Author Ralf Herbrich is a Postdoctoral Researcher in the Machine Learning and Perception Group at Microsoft Research Cambridge and a Research Fellow of Darwin College, University of Cambridge.
A Customer said Fascinating. An fine introduction to statistical learning theory! While the audio book format is certainly an unorthodox choice, the breathy, Jessica Rabbit-style narration turns out to be a boon when getting to grips with algorithmic stability and PAC bounds. First rate!