TY - JOUR
ID - 3036
TI - Support vector regression with random output variable and probabilistic constraints
JO - Iranian Journal of Fuzzy Systems
JA - IJFS
LA - en
SN - 1735-0654
AU - Abaszade, Maryam
AU - Effati, Sohrab
AD - Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran
AD - Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
Y1 - 2017
PY - 2017
VL - 14
IS - 1
SP - 43
EP - 60
KW - Probabilistic constraints
KW - Support vector machine
KW - Support Vector Regression
KW - Quadratic programming
KW - Probability function
KW - Monte Carlo simulation
DO - 10.22111/ijfs.2017.3036
N2 - 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.
UR - http://ijfs.usb.ac.ir/article_3036.html
L1 - http://ijfs.usb.ac.ir/article_3036_7fa269af1f035bda8d625aada763cf7a.pdf
ER -