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.
@article{arxiv.1803.02388,
title = {Learning SMaLL Predictors},
author = {Vikas K. Garg and Ofer Dekel and Lin Xiao},
journal= {arXiv preprint arXiv:1803.02388},
year = {2018}
}