Title :
A new Support Vector classification algorithm with parametric-margin model
Author :
Hao, Pei-Yi ; Tsai, Lung-Biao ; Lin, Min-Shiu
Author_Institution :
Dept. of Inf. Manage., Nat. Kaohsiung Univ. of Appl. Sci., Kaohsiung
Abstract :
In this paper, a new algorithm for Support Vector classification is described. It is shown how to use the parametric margin model with non-constant radius. This is useful in many cases, especially when the noise is heteroscedastic, that is, where it depends on x. Moreover, for a priori chosen v, the proposed new SV classification algorithm has advantage of using the parameter 0 les v les 1 on controlling the number of support vectors. To be more precise, v is an upper bound on the fraction of margin errors and a lower bound of the fraction of support vectors. Hence, the selection of v is more intuitive. The algorithm is analyzed theoretically and experimentally.
Keywords :
pattern classification; support vector machines; heteroscedastic noise; parametric-margin model; support vector classification algorithm; support vector machine; Algorithm design and analysis; Classification algorithms; Function approximation; Information management; Pattern classification; Quadratic programming; Shape; Support vector machine classification; Support vector machines; Upper bound;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4633826
