Name
Abstract | ||
|---|---|---|
Support vector machine (SVM) is not suitable for classification on large data sets due to its training complexity. Convex hull can simplify SVM training, however the classification accuracy becomes lower when there are inseparable points. This paper introduces a novel method for SVM classification, called convex-concave hull. After a grid processing, the convex hull is used to find extreme points. Then we detect a concave (non-convex) hull, the vertices of it are used to train SVM. We applied the proposed method on several problems. Experimental results demonstrate that our approach has good classification accuracy while the training is significantly faster than other state of the art methods. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICDMW.2012.76 | ICDM Workshops |
Keywords | Field | DocType |
convex hull,novel method,support vector machine,training complexity,art method,good classification accuracy,classification accuracy,convex-concave hull,svm training,svm classification,set theory,computational complexity,grid computing,support vector machines,learning artificial intelligence | Extreme point,Structured support vector machine,Pattern recognition,Computer science,Support vector machine,Convex hull,Regular polygon,Artificial intelligence,Output-sensitive algorithm,Hull,Machine learning,Computational complexity theory | Conference |
ISSN | Citations | PageRank |
2375-9232 | 1 | 0.36 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Asdrubal Lopez-Chau | 1 | 4 | 2.77 |
| Xiaoou Li | 2 | 550 | 61.95 |
| Wen Yu | 3 | 283 | 22.70 |