The nature of statistical learning theory
The nature of statistical learning theory
Playing billiards in version space
Neural Computation
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Machine Learning
Machine Learning
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
Analytic center of spherical shells and its application to analytic center machine
Computational Optimization and Applications
Stock index prediction based on the analytical center of version space
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
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Support vector machines have recently attracted much attention in the machine learning and optimization communities for their remarkable generalization ability. The support vector machine solution corresponds to the center of the largest hypersphere inscribed in the version space. Recently, however, alternative approaches (Herbrich, Graepel, & Campbell, In Proceedings of ESANN 2000) have suggested that the generalization performance can be further enhanced by considering other possible centers of the version space like the center of gravity. However, efficient methods for calculating the center of gravity of a polyhedron are lacking. A center that can be computed efficiently using Newton's method is the analytic center of a convex polytope. We propose an algorithm, that finds the hypothesis that corresponds to the analytic center of the version space. We refer to this type of classifier as the analytic center machine (ACM). Preliminary experimental results are presented for which ACMs outperform support vector machines.