A Conceptualization of Distributed Computation for Machine Learning: The Voting Algorithm
Journal Title: American Journal of Engineering and Applied Sciences - Year 2017, Vol 10, Issue 1
Abstract
Abstract This paper describes a voting algorithm that can be used to find the most optimal solution to clustering problems in machine learning. As part of the family of algorithms known as Condorcet methods, the voting algorithm is used to choose a particular candidate, even in the absence of a definitive majority. The algorithm proceeds in two steps: Renormalization and reconciliation. In the renormalization step all probability measure are reset so that the ensemble probability is always unity. In the reconciliation step a best choice is made based on the renormalized data. The result showed an excellent performance due to the use of linear time computations. Copyright © 2017 Talal Talib Jameel. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Authors and Affiliations
Talal Talib Jameel
Keywords
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- DOI 10.3844/ajeassp.2017.151.155
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