Name
Abstract | ||
|---|---|---|
Support vector algorithms try to maximize the shortest distance between sample points and discrimination hyperplane. This paper suggests the total margin algorithms which consider the distance between all data points and the separating hyperplane. The method extends and modifies the existing algorithms. Experimental studies show that the total margin algorithms provide good performance comparing with the existing support vector algorithms. |
Year | Venue | Keywords |
|---|---|---|
2004 | IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS | support vector machine, generalization ability, soft margin, slack variable, total margin, surplus variable |
Field | DocType | Volume |
Slack variable,Computer science,Support vector machine,Artificial intelligence | Journal | E87D |
Issue | ISSN | Citations |
5 | 1745-1361 | 3 |
PageRank | References | Authors |
0.55 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Min Yoon | 1 | 34 | 10.38 |
| Yeboon Yun | 2 | 38 | 8.60 |
| Hirotaka Nakayama | 3 | 122 | 23.18 |