Title :
A general formulation for support vector machines
Author :
Chu, Wei ; Keerthi, S. Sathiya ; Ong, Chong Jin
Author_Institution :
Dept. of Mech. Eng., Nat. Univ. of Singapore, Singapore
Abstract :
In this paper, we derive a general formulation of support vector machines for classification and regression respectively. Le, loss function is proposed as a patch of L1 and L2 soft margin loss functions for classifier, while soft insensitive loss function is introduced as the generalization of popular loss functions for regression. The introduction of the two loss functions results in a general formulation for support vector machines.
Keywords :
learning (artificial intelligence); minimisation; pattern classification; quadratic programming; support vector machines; classification; general formulation; minimization problem; quadratic programming; regression; soft insensitive loss function; soft margin loss functions; supervised learning; support vector machines; Hilbert space; Kernel; Lagrangian functions; Mechanical engineering; Quadratic programming; Scholarships; Static VAr compensators; Supervised learning; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on
Print_ISBN :
981-04-7524-1
DOI :
10.1109/ICONIP.2002.1201949
