International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Since Year 2012 | Open Access | Fully Refereed | Peer Reviewed

ISSN: 2319-7064




Downloads: 121

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


How to Cite this Article?

Satwik Kumar Shiri, Satyam Thusu, "Machine Learning using MapReduce", International Journal of Science and Research (IJSR), Volume 6 Issue 6, June 2017, pp. 2467-2471, https://www.ijsr.net/get_abstract.php?paper_id=ART20174894

How to Share this Article?

Enter Your Email Address




Similar Articles with Keyword 'MapReduce'

Downloads: 102

Dissertation Chapters, Computer Science & Engineering, India, Volume 4 Issue 7, July 2015

Pages: 1721 - 1725

Secured Load Rebalancing for Distributed Files System in Cloud

Jayesh D. Kamble | Y. B. Gurav [8]

Share this Article

Downloads: 105

M.Tech / M.E / PhD Thesis, Computer Science & Engineering, India, Volume 4 Issue 3, March 2015

Pages: 2133 - 2136

One Class Clustering Tree for Implementing Many to Many Data Linkage

Ravi R [9] | Michael G [4]

Share this Article


Top