Name
Abstract | ||
|---|---|---|
Support vector machine (SVM) is a widely used maximum margin classifier, but the classification performance is largely affected by outliers. In this paper, we propose a novel multi-class SVM method to reduce the influence of outliers on the classification performance. Our proposed method includes an efficient optimization model via considering the within-class scatter and an optimization way. Specifically, the method is based on one assumption that penalizing the within-class scatter can reduce the number of misclassified outliers near the decision boundary, because data points of each class could be compacted by the within-class penalty. Experiments on benchmark databases demonstrate the effectiveness of the assumption and the proposed method. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/ICIP.2015.7351302 | ICIP |
Keywords | Field | DocType |
SVM,multi-class,within-class scatter | Structured support vector machine,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Relevance vector machine,Margin classifier,Decision boundary,Machine learning | Conference |
Volume | ISSN | Citations |
2015-December | 1522-4880 | 1 |
PageRank | References | Authors |
0.35 | 10 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaoshuang Shi | 1 | 117 | 14.35 |
| Guo Zhenhua | 2 | 1658 | 67.47 |
| Yang Yu-Jiu | 3 | 89 | 19.30 |
| Lin Yang | 4 | 1291 | 116.88 |