A Note on the Generalization Performance of Kernel Classifiers with Margin
| Data(s) |
20/10/2004
20/10/2004
01/05/2000
|
|---|---|
| Resumo |
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 family of loss functions where the SVM one belongs to. Bounds that use functions of margin distributions (i.e. functions of the slack variables of SVM) are derived. |
| Formato |
9 p. 1149066 bytes 253797 bytes application/postscript application/pdf |
| Identificador |
AIM-1681 CBCL-184 |
| Idioma(s) |
en_US |
| Relação |
AIM-1681 CBCL-184 |
| Palavras-Chave | #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. |
