Improving the speed of support vector regression using regularized least square regression

The Regularized Least Square (RLS) method is one of the fastest function estimation methods, but it is too sensitive to noise. Against this, ε-insensitive Support Vector Regression (ε-SVR) is robust to noise but doesn't have a good runtime. ε-SVR supposes that the noise level is at most ε. Therefore, the center of a tube with radius ε, which is used as the estimated function, is determined in a way that the training data are located in that tube. Therefore, this method is robust to such noisy data. In this paper, to improve the runtime of ε-SVR, first, an initial estimated function is obtained using the RLS method. Then, unlike the ε-SVR model, which uses all the data to determine the lower and upper limits of the tube, our proposed method uses the initial estimated function for determining the tube and the final estimated function. Strictly speaking, the data below and above the initial estimated function are used to estimate the upper and lower limits of the tube, respectively. Thus, the number of the model constraints and, consequently, the model runtime are reduced. The experiments carried out on 15 benchmark data sets confirm that our proposed method is faster than ε-SVR, ε-TSVR and pair v-SVR, and its accuracy are comparable with that of ε-SVR, ε-TSVR and pair v-SVR. © 2020 International Information and Engineering Technology Association. All rights reserved.