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arXiv · 1803.02388

Learning SMaLL Predictors

Vikas K. Garg·Ofer Dekel·Lin Xiao
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Abstract

We present a new machine learning technique for training small resource-constrained predictors. Our algorithm, the Sparse Multiprototype Linear Learner (SMaLL), is inspired by the classic machine learning problem of learning $k$-DNF Boolean formulae. We present a formal derivation of our algorithm and demonstrate the benefits of our approach with a detailed empirical study.

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Vikas K. Garg, Ofer Dekel, Lin Xiao. 2018-03-06. Learning SMaLL Predictors. https://arxiv.org/abs/1803.02388

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