Title :
Adaptive support vector machine with homogeneous decision function
Author :
Li, Xiaohuan ; Yang, Zhixia
Author_Institution :
Coll. of Math. & Syst. Sci., Xinjiang Univ., Urumqi, China
Abstract :
In this paper we propose a new algorithm called adaptive support vector machine with homogeneous decision function. In our algorithm, the distribution of samples has been taken into consideration, so that the margin of bounding hyperplanes is as large as possible. Moreover, we introduce a pair of parameters v+ and v- to control bounds of the fractions of support vectors and margin errors. We also show that our algorithm can deal with imbalanced data effectively. Experiments on several artificial and UCI datasets indicate the proposed algorithm has good classification accuracy.
Keywords :
support vector machines; UCI datasets; adaptive support vector machine; bounding hyperplanes; classification accuracy; homogeneous decision function; margin errors; Accuracy; Covariance matrix; Educational institutions; Gaussian distribution; Kernel; Support vector machines; Training;
Conference_Titel :
Natural Computation (ICNC), 2012 Eighth International Conference on
Conference_Location :
Chongqing
Print_ISBN :
978-1-4577-2130-4
DOI :
10.1109/ICNC.2012.6234557