Title :
Improved density-induced support vector data description
Author :
Yin, Feng ; Huang, Guang-xin
Author_Institution :
Coll. of Sciene, Sichuan Univ. of Sci. & Eng., Zigong, China
Abstract :
Support vector data description (SVDD) is a data description method which can give the target data set a spherically shaped description. A density-induced SVDD (D-SVDD) has been proposed to improve the SVDD. However, the dual optimization problem of the D-SVDD is not a simple optimization problem which makes the D-SVDD be not an easy data description method. This paper presents an improved density-induced SVDD. The hyper-spherically shaped boundary of our method resorts to a well-known quadratic programming problem, thus the proposed data description method improves the D-SVDD.
Keywords :
quadratic programming; density-induced SVDD; density-induced support vector data description; dual optimization problem; hyper-spherically shaped boundary; quadratic programming problem; shaped description; Educational institutions; Kernel; Machine learning; Quadratic programming; Support vector machines; Training; D-SVDD; Hyper-spherically shaped boundary; One-class classification; Support vector data description;
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2011 International Conference on
Conference_Location :
Guilin
Print_ISBN :
978-1-4577-0305-8
DOI :
10.1109/ICMLC.2011.6016770