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Research Paper | Computer Science & Engineering | India | Volume 6 Issue 6, June 2017

Machine Learning using MapReduce

Satwik Kumar Shiri | Satyam Thusu

Abstract: Machine learning methods often improve their accuracy by using models with more parameters trained on large numbers of data sets. Building such models on a single machine is often impractical because of expansive measure of calculation required. In this paper, we focus on developing a general technique for parallel programming of some of the machine learning algorithms. Our work is in distinct to the tradition in machine learning of designing ways to speed up a single algorithm at a time. We show that algorithms that fit the Statistical Query model can be composed in a certain summation form, which allows them to be effectively parallelized. The central idea of this approach is to allow a future programmer or user to accelerate machine learning applications.

Keywords: MapReduce, Machine Learning, Large Data Sets, Algorithms

Edition: Volume 6 Issue 6, June 2017,

Pages: 2467 - 2471

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