A full smooth semi-support vector machine based on the cubic spline function

Author

Jinggai Ma ; Xiaodan Zhang

Author_Institution

Sch. of Mathematic & Phys., Univ. of Sci. & Technol. Beijing, Beijing, China

fYear

2013

fDate

16-18 Dec. 2013

Firstpage

650

Lastpage

655

Abstract

The non-smooth problem for the semi-supervised support vector machine optimization model is studied. Since the objective function of the unstrained semi-supervised vector machine model is a non-smooth function. Most fast optimization algorithms can not be applied to solve the semi-supervised vector machine model. We propose a full smooth cubic spline function to approximate the symmetric hinge loss function. The Broyden-Fletcher-Goldfarb-Shanno(BFGS) algorithm is used to solve the new model. The experimental results show that the new model has a better classification performance.

Keywords

function approximation; optimisation; splines (mathematics); support vector machines; BFGS algorithm; Broyden-Fletcher-Goldfarb-Shanno algorithm; classification performance; fast optimization algorithms; full smooth cubic spline function; full smooth semisupport vector machine; nonsmooth function; nonsmooth problem; semisupervised support vector machine optimization model; symmetric hinge loss function approximation; unstrained semisupervised vector machine model; Approximation methods; Educational institutions; Fasteners; Mathematical model; Optimization; Splines (mathematics); Support vector machines; classification; semi-supervised; smooth; spline; support vector machine;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Informatics (BMEI), 2013 6th International Conference on

Conference_Location

Hangzhou

Print_ISBN

978-1-4799-2760-9

Type

conf

DOI

10.1109/BMEI.2013.6747020

Filename

6747020