Stochastic support vector regression with probabilistic constraints |
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Authors: | Maryam Abaszade Sohrab Effati |
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Affiliation: | 1.Department of Statistics,Ferdowsi University of Mashhad,Mashhad,Iran;2.Department of Applied Mathematics,Ferdowsi University of Mashhad,Mashhad,Iran;3.Center of Excellence of Soft Computing and Intelligent Information Processing (SCIIP),Ferdowsi University of Mashhad,Mashhad,Iran |
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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. |
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