A Brief Review of Big Data Analytics Based on Machine Learning

A. Ali, M. Abdullah, Shams N. Abdul-wahab, Mohammad Alsajri
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引用次数: 4

Abstract

Owing to the exponential expansion in the data size, fast and efficient systems of analysis are extremely needed. The traditional algorithms of machine learning face the challenge of learning bottlenecks such as; human participation, time, and the accuracy of prediction. But, the efficient and fast methods of dynamic learning offer considerable advantages like lower human participation, rapid algorithms of learning, and easiness implementation. This review paper presents the researches with a brief display for recently existing works in big data analytics and the effective algorithms of machine learning, furthermore, the issues of resources allocation in big data
基于机器学习的大数据分析综述
由于数据量呈指数级增长,迫切需要快速高效的分析系统。传统的机器学习算法面临着学习瓶颈的挑战,例如;人的参与,时间和预测的准确性。但是,高效、快速的动态学习方法具有人工参与率低、学习算法快速、易于实现等显著优势。本文简要介绍了大数据分析领域的最新研究成果和机器学习的有效算法,以及大数据中的资源分配问题
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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CiteScore
4.30
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0.00%
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