A GPU-Architecture Optimized Hierarchical Decomposition Algorithm for Support Vector Machine Training

Abstract

In the last decade, several GPU implementations of Support Vector Machine (SVM) training with nonlinear kernels were published. Some of them even with source codes. The most effective ones are based on Sequential Minimal Optimization (SMO). They decompose the restricted quadratic problem into a series of smallest possible subproblems, which are then solved… (More)
DOI: 10.1109/TPDS.2017.2731764

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