Convex sets as prototypes for classifying patterns
Engineering Applications of Artificial Intelligence
Coresets for polytope distance
Proceedings of the twenty-fifth annual symposium on Computational geometry
A novel geometric approach to binary classification based on scaled convex hulls
IEEE Transactions on Neural Networks
Algorithms for the Computation of Reduced Convex Hulls
AI '09 Proceedings of the 22nd Australasian Joint Conference on Advances in Artificial Intelligence
A modified support vector machine and its application to image segmentation
Image and Vision Computing
Computer Methods and Programs in Biomedicine
A game-theoretic approach to weighted majority voting for combining SVM classifiers
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
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Geometric methods are very intuitive and provide a theoretically solid viewpoint to many optimization problems. SVM is a typical optimization task that has attracted a lot of attention over the recent years in many Pattern Recognition and Machine Learning tasks. In this work, we exploit recent results in Reduced Convex Hulls (RCH) and apply them to a Nearest Point Algorithm (NPA) leading to an elegant and efficient solution to the general (linear and nonlinear, separable and non-separable) SVM classification task.