Using More Data to Speed-up Training Time
Machine Learning
2011-06-16 v2 Machine Learning
Abstract
In many recent applications, data is plentiful. By now, we have a rather clear understanding of how more data can be used to improve the accuracy of learning algorithms. Recently, there has been a growing interest in understanding how more data can be leveraged to reduce the required training runtime. In this paper, we study the runtime of learning as a function of the number of available training examples, and underscore the main high-level techniques. We provide some initial positive results showing that the runtime can decrease exponentially while only requiring a polynomial growth of the number of examples, and spell-out several interesting open problems.
Cite
@article{arxiv.1106.1216,
title = {Using More Data to Speed-up Training Time},
author = {Shai Shalev-Shwartz and Ohad Shamir and Eran Tromer},
journal= {arXiv preprint arXiv:1106.1216},
year = {2011}
}