A Note on the Generalization Performance of Kernel Classifiers with Margin

A Note on the Generalization Performance of Kernel Classifiers with Margin

We present distribution independent bounds on the generalization misclassification performance of a family of kernel classifiers with margin. Support Vector Machine classifiers (SVM) stem out of this class of machines. The bounds are derived through computations of the $V_gamma$ dimension of a famil...

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Bibliographic Details
Main Authors: Evgeniou, Theodoros, Pontil, Massimiliano
Language:en_US
Published: 2004
Subjects:
AI
MIT
Artificial Intelligence
missing data
mixture models
statistical learning
EM algorithm
neural networks
kernel classifiers
Support Vector Machine
regularization networks
statistical learning theory
V-gamma dimension.
Online Access:http://hdl.handle.net/1721.1/7169

Internet

http://hdl.handle.net/1721.1/7169

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