Title :
M-SVC (mixed-norm SVC) - a novel form of support vector classifier
Author_Institution :
Dept. of Electron. Eng., Nat. Yunlin Univ. of Sci. & Technol.
Abstract :
Support vector machines are currently a very active research area within machine learning, data mining, and other related research communities. This paper presents a new form of generalized support vector classifier that includes both the 1-norm and 2-norm SVCs as its special cases. We refer to this new SVC as the mixed-norm support vector classifier (or m-SVC). The dual form of the m-SVC optimization problem is explicitly derived. A decomposition-type algorithm is described to solve the large sample-size m-SVC problem. We give some examples to demonstrate the solvability of the m-SVC formulation and to illustrate the relations among the m-SVC and convectional 1-norm and 2-norm SVCs
Keywords :
data mining; learning (artificial intelligence); optimisation; support vector machines; 1-norm SVC; 2-norm SVC; data mining; decomposition-type algorithm; machine learning; mixed-norm SVC; mixed-norm support vector classifier; support vector machines; Data mining; Functional analysis; Image analysis; Kernel; Machine learning; Optical character recognition software; Static VAr compensators; Support vector machine classification; Support vector machines; Time series analysis;
Conference_Titel :
Circuits and Systems, 2006. ISCAS 2006. Proceedings. 2006 IEEE International Symposium on
Conference_Location :
Island of Kos
Print_ISBN :
0-7803-9389-9
DOI :
10.1109/ISCAS.2006.1693321
