Title :
A Comparative Study of Four Smooth Support Vector Regressions
Author :
Shen, Jindong ; Cao, Feilong
Author_Institution :
Dept. of Inf. & Math. Sci., China Jiliang Univ., Hangzhou, China
Abstract :
Researching smooth support vector machine for regression (SVR) is an active field in data mining. This paper presents a comparison among four smooth SVRs , epsilon-SSVR, 1st-order polynomial smooth SVR (1PSSVR), 2nd-order polynomial smooth SVR (2PSSVR) and third-order spline smooth SVR (TSSSVR). Accuracy, convergence speed and computational complexity of these regressions are compared.
Keywords :
computational complexity; data mining; regression analysis; splines (mathematics); support vector machines; 2nd-order polynomial smooth SVR; computational complexity; convergence speed; data mining; epsilon-SSVR; lst-order polynomial smooth SVR; support vector machine for regression; third-order spline smooth SVR; Computational complexity; Convergence; Data mining; Error correction; Fitting; Mathematical programming; Polynomials; Smoothing methods; Spline; Support vector machines;
Conference_Titel :
Information Engineering and Computer Science, 2009. ICIECS 2009. International Conference on
Conference_Location :
Wuhan
Print_ISBN :
978-1-4244-4994-1
DOI :
10.1109/ICIECS.2009.5364040
