Name
Abstract | ||
|---|---|---|
In this paper, a generalization of support vector machines is explored where it is considered that input vectors have different @?"p norms for each class. It is proved that the optimization problem for binary classification by using the maximal margin principle with @?"p and @?"q norms only depends on the @?"p norm if 1@?p@?q. Furthermore, the selection of a different bias in the classifier function is a consequence of the @?"q norm in this approach. Some commentaries on the most commonly used approaches of SVM are also given as particular cases. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.camwa.2011.03.071 | Computers & Mathematics with Applications |
Keywords | DocType | Volume |
different metrics,support vector machine,particular case,classifier function,maximal margin principle,learning machine,q norm,pattern recognition,ℓ p norm,optimization problem,p norm,binary classification,input vector,different bias | Journal | 61 |
Issue | ISSN | Citations |
9 | Computers and Mathematics with Applications | 4 |
PageRank | References | Authors |
0.46 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. Gonzalez-Abril | 1 | 153 | 8.48 |
| F. Velasco | 2 | 106 | 5.83 |
| J. A. Ortega | 3 | 99 | 7.03 |
| L. Franco | 4 | 6 | 1.19 |