An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Duality and Geometry in SVM Classifiers
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Simple solvers for large quadratic programming tasks
PR'05 Proceedings of the 27th DAGM conference on Pattern Recognition
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While usually SVM training tries to solve the dual of the standard SVM minimization problem, alternative algorithms that solve the Nearest Point Problem (NPP) for the convex hulls of the positive and negative samples have been shown to also provide effective SVM training. They are variants of the Mitchell---Demyanov---Malozemov (MDM) algorithm and although they perform a two vector weight update, they must compute 4 possible updating vectors before deciding which ones to use. In this work we shall propose a 4---vector version of the MDM algorithm that requires substantially less iterations than the previous 2---vector MDM algorithms and leads to faster training if kernel operations are cached.