Name
Abstract | ||
|---|---|---|
In this paper, we propose a midpoint-validation method which improves the generalization of Support Vector Machine. The proposed method creates midpoint data, as well as a turning adjustment parameter of Support Vector Machine using midpoint data and previous training data. We compare its performance with the original Support Vector Machine, Multilayer Perceptron, Radial Basis Function Neural Network and also tested our proposed method on several benchmark problems. The results obtained from the simulation shows the effectiveness of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1093/ietisy/e91-d.7.2095 | IEICE Transactions |
Keywords | Field | DocType |
support vector machine classification,midpoint data,support vector machine,midpoint-validation method,multilayer perceptron,previous training data,adjustment parameter,original support,vector machine,radial basis function neural | Structured support vector machine,Midpoint,Pattern recognition,Computer science,Support vector machine,Algorithm,Multilayer perceptron,Artificial intelligence,Relevance vector machine,Kernel method,Artificial neural network,Perceptron | Journal |
Volume | Issue | ISSN |
E91-D | 7 | 1745-1361 |
Citations | PageRank | References |
5 | 0.58 | 6 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiroki Tamura | 1 | 72 | 21.29 |
| Koichi Tanno | 2 | 57 | 22.05 |