Name
Abstract | ||
|---|---|---|
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 Vγ 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. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/3-540-40992-0_23 | Algorithmic Learning Theory |
Keywords | Field | DocType |
independent bound,margin distribution,slack variable,use function,loss function,generalization misclassification performance,support vector machine classifier,generalization performance,present distribution,kernel classifiers,kernel classifier,mixture models,em algorithm,neural networks,ai,missing data,support vector machine,artificial intelligence | Kernel (linear algebra),Statistical learning theory,Slack variable,Computer science,Expectation–maximization algorithm,Support vector machine,Algorithm,Artificial intelligence,Missing data,Artificial neural network,Machine learning,Mixture model | Conference |
Volume | ISSN | ISBN |
1968 | 0302-9743 | 3-540-41237-9 |
Citations | PageRank | References |
1 | 1.94 | 7 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Theodoros Evgeniou | 1 | 3005 | 219.65 |
| Massimiliano Pontil | 2 | 5820 | 472.96 |