Learning Kernel Classifiers: Theory and Algorithms

Learning Kernel Classifiers: Theory and Algorithms

by Ralf Herbrich
     
 

An overview of the theory and application of kernel classification methods.See more details below

Overview

An overview of the theory and application of kernel classification methods.

Product Details

ISBN-13:
9780262083065
Publisher:
MIT Press
Publication date:
12/15/2001
Series:
Adaptive Computation and Machine Learning series
Edition description:
New Edition
Pages:
384
Product dimensions:
7.00(w) x 9.00(h) x 1.10(d)
Age Range:
18 Years

Table of Contents

Series Foreword
Preface
1Introduction1
ILearning Algorithms
2Kernel Classifiers from a Machine Learning Perspective17
3Kernel Classifiers from a Bayesian Perspective73
IILearning Theory
4Mathematical Models of Learning115
5Bounds for Specific Algorithms163
IIIAppendices
ATheoretical Background and Basic Inequalities199
BProofs and Derivations - Part I253
CProofs and Derivations - Part II281
DPseudocodes321
List of Symbols331
References339
Index357

Read More

Customer Reviews

Average Review:

Write a Review

and post it to your social network

     

Most Helpful Customer Reviews

See all customer reviews >