Title :
A Smoothing Function for 1-norm Support Vector Machines
Author :
Ruo-peng, Wang ; Hong-min, Xu
Author_Institution :
Dept. of Math. & Phys., Beijing Inst. of Petro-Chem. Technol., Beijing, China
Abstract :
In this paper, a novel smoothing function method for the 1-norm support vector regression (SVR for short) is proposed and an attempt to overcome some drawbacks of former method which are complex, subtle, and sometimes difficult to implement. The model of smoothing support vector machine (SVM) based on 1-norm is provided from the optimization problem, yet it is discrete programming. With the smoothing technique and optimality knowledge, the discrete programming is changed into a continuous programming. Experimental results show that the algorithm is easy to implement and this method is fast and insensitive to initial point. Theory analysis illustrate that smoothing function method for 1-norm SVM are feasible and effective.
Keywords :
optimisation; regression analysis; support vector machines; 1-norm support vector regression; continuous programming; discrete programming; optimization problem; smoothing function; support vector machines; Equations; Kernel; Machine learning algorithms; Mathematics; Neural networks; Physics computing; Predictive models; Smoothing methods; Support vector machine classification; Support vector machines; Support Vector Machine (SVM); algorithm; optimization; smoothing function;
Conference_Titel :
Natural Computation, 2009. ICNC '09. Fifth International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-0-7695-3736-8
DOI :
10.1109/ICNC.2009.387
