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A Survey of Online Learning Algorithm
Abstract
Online learning is an active research area, which emerges in the last decade and has been considered as a subset of machine learning. Although, there is limited advancements has been made due to its high processing environment ultimately delaying its real-time applicability. In this study, we studied the various techniques with which the online learning algorithms have been able to successfully embark the results over the World Wide Web. It is considered to be a challenging research topic because of the problem of bandwidth and latency where the non-linear classifiers are required to be used in combination of weights that are to be learned simultaneously for the processing of an online job.
Keywords: Online learning, relative loss bounds, bandwidth, latency, multi-processing
Cite this Article
Bharti Patel, Rajesh Tiwari, A Survey of Online Learning Algorithm, Recent Trends in Parallel Computing. 2015; 2(1): 6–9p.
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