The use of Big Data in Machine Learning Algorithm

Yew Kee Wong
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引用次数: 1

Abstract

In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Such minimal human intervention can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyse some of the different machine learning algorithms and methods which can be applied to big data analysis, as well as the opportunities provided by the application of big data analytics in various decision making domains.
大数据在机器学习算法中的应用
在信息时代,决策者手头有大量的数据。大数据是指数据集不仅大,而且种类和速度也很高,这使得使用传统工具和技术难以处理。由于这些数据的快速增长,需要研究和提供解决方案,以便从这些数据集中处理和提取价值和知识。机器学习是一种数据分析方法,可以自动构建分析模型。它是人工智能的一个分支,基于这样一种理念:系统可以从数据中学习,识别模式,并在最少的人为干预下做出决策。这种最小的人为干预可以通过大数据分析来提供,这是高级分析技术在大数据上的应用。本文旨在分析可以应用于大数据分析的一些不同的机器学习算法和方法,以及大数据分析在各种决策领域的应用所提供的机会。
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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