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.
['Leu-Shing Lan']
M-SVC (mixed-norm SVC) - a novel form of support vector classifier