A Novel Sphere-Based Maximum Margin Classification Method

Support vector data description (SVDD) aims at constructing an optimal hypersphere regarded as a data description for a dataset while support vector classification (SVC) aims at separating data of two classes without providing a data description. This paper proposes a unified approach to both SVDD and SVC that aims at separating data of two classes and at the same time provides a data description. A trade off parameter is introduced to control the balance between describing the data and maximising the margin. Experimental results are provided to evaluate the proposed approach.