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arXiv · 1806.10179

Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training

Sahar Qaadan·Tobias Glasmachers
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Abstract

Budgeted Stochastic Gradient Descent (BSGD) is a state-of-the-art technique for training large-scale kernelized support vector machines. The budget constraint is maintained incrementally by merging two points whenever the pre-defined budget is exceeded. The process of finding suitable merge partners is costly; it can account for up to 45% of the total training time. In this paper we investigate computationally more efficient schemes that merge more than two points at once. We obtain significant speed-ups without sacrificing accuracy.

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Sahar Qaadan, Tobias Glasmachers. 2018-06-26. Multi-Merge Budget Maintenance for Stochastic Gradient Descent SVM Training. https://arxiv.org/abs/1806.10179

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