Convex Hull in Feature Space for Support Vector Machines

Some important geometric properties of Support Vector Machines (SVM) have been studied in the last few years, allowing researchers to develop several algorithmic aproaches to the SVM formulation for binary pattern recognition. One important property is the relationship between support vectors and the Convex Hulls of the subsets containing the classes, in the separable case. We propose an algorithm for .nding the extreme points of the Convex Hull of the data points in feature space. The key of the method is the construction of the Convex Hull in feature space using an incremental procedure that works using kernel functions and with large datasets. We show some experimental results.