Name
Abstract | ||
|---|---|---|
In this paper, a new version of Support Vector Machine (SVM) is proposed which any of training samples are considered the random variables. Hence, in order to achieve robustness, the constraint in SVM must be replaced with probability of constraint. In this new model, by applying the nonparametric statistical methods, we obtain the optimal separating hyperplane by solving a quadratic optimization problem. Afterwards, we present the least squares model of our proposed method. The efficiency of our proposed method is shown by several examples for both cases (linear and nonlinear) with probabilistic constraints. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/s00357-018-9282-x | Journal of Classification |
Keywords | Field | DocType |
Probabilistic constraints,Support Vector Machine,Least squares Support Vector Machine,Mathematical expectation,Plug-in estimator,Monte Carlo simulation | Least squares,Random variable,Least squares support vector machine,Support vector machine,Algorithm,Robustness (computer science),Nonparametric statistics,Quadratic programming,Probabilistic logic,Statistics,Mathematics | Journal |
Volume | Issue | ISSN |
36.0 | 1.0 | 1432-1343 |
Citations | PageRank | References |
1 | 0.35 | 26 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maryam Abaszade | 1 | 4 | 0.73 |
| Effati Sohrab | 2 | 276 | 30.31 |