Title :
A unified model for support vector machine and support vector data description
Author :
Le, Trung ; Tran, Dat ; Ma, Wanli ; Sharma, Dharmendra
Author_Institution :
Fac. of Inf. Sci. & Eng., Univ. of Canberra, Canberra, ACT, Australia
Abstract :
Support vector machine (SVM) and support vector data description (SVDD) are the well-known kernel-based methods for pattern classification. SVM constructs an optimal hyperplane whereas SVDD constructs an optimal hypersphere to separate data between two classes. SVM and SVDD have been compared in pattern classification experiments however there is no theoretical work on comparison between these methods. This paper presents a new theoretical model to unify SVM and SVDD. The proposed model constructs two optimal points to generate a general decision boundary which can be transformed to hyperplane for SVM or hypersphere for SVDD.
Keywords :
data description; pattern classification; support vector machines; SVDD; SVM; general decision boundary; hyperplane; kernel-based methods; optimal hypersphere; optimal points; pattern classification; support vector data description; support vector machine; Australia; Mathematical model; Optimization; Support vector machines; Training; Trajectory; Vectors; Novelty detection; one-class classification; spherically shaped boundary; support vector data description;
Conference_Titel :
Neural Networks (IJCNN), The 2012 International Joint Conference on
Conference_Location :
Brisbane, QLD
Print_ISBN :
978-1-4673-1488-6
Electronic_ISBN :
2161-4393
DOI :
10.1109/IJCNN.2012.6252642