A Conceptualization of Distributed Computation for Machine Learning: The Voting Algorithm

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.