The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
The quickhull algorithm for convex hulls
ACM Transactions on Mathematical Software (TOMS)
Reducing the run-time complexity in support vector machines
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Support vector machines: training and applications
Support vector machines: training and applications
A fast iterative nearest point algorithm for support vector machine classifier design
IEEE Transactions on Neural Networks
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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 finding 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.