Name
Abstract | ||
|---|---|---|
Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, we introduce a novel model of SVR in which any training samples containing inputs and outputs are considered the random variables with known or unknown distribution functions. Constraints occurrence have a probability density function which helps to obtain maximum margin and achieve robustness. The optimal hyperplane regression can be obtained by solving a quadratic optimization problem. The proposed method is illustrated by several experiments including artificial data sets and real-world benchmark data sets. |
Year | DOI | Venue |
|---|---|---|
2018 | https://doi.org/10.1007/s10489-017-0964-6 | Appl. Intell. |
Keywords | Field | DocType |
Support vector machine,Support vector regression,Margin maximization,Mathematical expectation,Plug-in estimator,Monte Carlo simulation | Structured support vector machine,Random variable,Computer science,Artificial intelligence,Quadratic programming,Mathematical optimization,Pattern recognition,Least squares support vector machine,Support vector machine,Relevance vector machine,Probability vector,Margin classifier,Machine learning | Journal |
Volume | Issue | ISSN |
48 | 1 | 0924-669X |
Citations | PageRank | References |
0 | 0.34 | 19 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Maryam Abaszade | 1 | 0 | 0.34 |
| Effati Sohrab | 2 | 276 | 30.31 |