Research Paper | Computer Science & Engineering | India | Volume 6 Issue 6, June 2017
Machine Learning using MapReduce
Satwik Kumar Shiri, Satyam Thusu
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
How to Cite this Article?
Satwik Kumar Shiri, Satyam Thusu, "Machine Learning using MapReduce", International Journal of Science and Research (IJSR), https://www.ijsr.net/search_index_results_paperid.php?id=ART20174894, Volume 6 Issue 6, June 2017, 2467 - 2471
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