support vector regression with random output variable and probabilistic constraints
نویسندگان
چکیده
support vector regression (svr) solves regression problems based on the concept of support vector machine (svm). in this paper, a new model of svr with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadratic optimization problem. the proposedmethod is illustrated by several simulated data and real data sets for both models (linear and nonlinear) with probabilistic constraints.
منابع مشابه
Support vector regression with random output variable and probabilistic constraints
Support Vector Regression (SVR) solves regression problems based on the concept of Support Vector Machine (SVM). In this paper, a new model of SVR with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. Using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...
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a Information Technology Supporting Center, Institute of Scientific and Technical Information of China No. 15 Fuxing Rd., Haidian District, Beijing 100038, China b School of Economics and Management, Beijing Forestry University No. 35 Qinghua East Rd., Haidian District, Beijing 100038, China College of Information and Electrical Engineering, China Agricultural University No. 17 Qinghua East Rd....
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عنوان ژورنال:
iranian journal of fuzzy systemsجلد ۱۴، شماره ۱، صفحات ۴۳-۶۰
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