Name
Abstract | ||
|---|---|---|
Novel multiclass approach that simultaneously constructs all required hyperplanes.Extensions of OvO and OvA multiclass SVM to second-order cone programming SVM.Best classification performance is achieved in experiments on benchmark datasets. This paper presents novel second-order cone programming (SOCP) formulations that determine a linear multi-class predictor using support vector machines (SVMs). We first extend the ideas of OvO (One-versus-One) and OvA (One-versus-All) SVM formulations to SOCP-SVM, providing two interesting alternatives to the standard SVM formulations. Additionally, we propose a novel approach (MC-SOCP) that simultaneously constructs all required hyperplanes for multi-class classification, based on the multi-class SVM formulation (MC-SVM). The use of conic constraints for each pair of training patterns in a single optimization problem provides an adequate framework for a balanced and effective prediction. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.ins.2015.10.016 | Information Sciences |
Keywords | Field | DocType |
Multi-class classification,Support vector machines,Second-order cone programming,Quadratic programming,Convex optimization | Second-order cone programming,Mathematical optimization,Support vector machine,Artificial intelligence,Hyperplane,Quadratic programming,Conic section,Convex optimization,Optimization problem,Mathematics,Machine learning,Multiclass classification | Journal |
Volume | Issue | ISSN |
330 | C | 0020-0255 |
Citations | PageRank | References |
6 | 0.43 | 23 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Julio López | 1 | 124 | 13.49 |
| Sebastián Maldonado | 2 | 508 | 32.45 |