Name
Abstract | ||
|---|---|---|
Support vector machines (SVMs) were designed for two-class classification problems, and multi-class classification problems have been solved by combining independently produced two-class decision functions. In this paper, we propose two multi-class Lagrangian Support Vector Machine(LSVM) algorithms using the quick and simple properties of LSVM. The experimental results in the linear and nonlinear cases indicate that the CPU running time of these two algorithms is shorter than that of the standard support vector machines, and their training correctness and testing correctness are almost identical. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-74205-0_92 | ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence |
Keywords | Field | DocType |
two-class decision function,support vector machine,multi-class classification problem,training correctness,nonlinear case,multi-class lagrangian support,two-class classification problem,standard support vector machine,vector machine algorithms,vector machine,multi class classification | Structured support vector machine,Nonlinear system,Lagrangian,Computer science,Correctness,Artificial intelligence,Central processing unit,Least squares support vector machine,Pattern recognition,Support vector machine,Algorithm,Relevance vector machine,Machine learning | Conference |
Volume | Issue | ISSN |
4682 LNAI | null | 16113349 |
Citations | PageRank | References |
0 | 0.34 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hua Duan | 1 | 110 | 19.58 |
| Quanchang Liu | 2 | 0 | 0.34 |
| Guoping He | 3 | 91 | 13.59 |
| Qingtian Zeng | 4 | 242 | 43.67 |