Online Learning Algorithm for Multi-Kernel Balancing of Computational Work Load

Ankush Rai

doi:10.37591/jowet.v1i3.277

Abstract

The online learning is an advancement made toward the extended machine learning approach where the application is web-based. Thus, the common problem to build a kernel based online perceptron algorithm is the amount of memory required to dynamically access for the so formed hypothesis derivable from the algorithm. As it tends to increase without a lower bound; also the computational load of such an algorithm increases linearly with the memory chunk used to build the hypothesis. Though, the previous works are focused towards discarding the instances of online hypothesis from the perceptron algorithm in order to reserve the computational work load within the bounded memory. Therefore, in this study we presented a work which dynamically creates an Online Chain Reaction Algorithm (OCRA) for keeping the checkpoint trees of hypothesis with the varying memory instances; which makes the online learning more dynamics and invariant of the pre-requisite amount of memory required.Keywords: online learning, kernel methods, distributed computation workload