Machine Learning for Big Data

S. M. S., K. Sasidaran
{"title":"Machine Learning for Big Data","authors":"S. M. S., K. Sasidaran","doi":"10.4018/978-1-7998-6673-2.CH004","DOIUrl":null,"url":null,"abstract":"Big data and machine learning currently play an important role in various applications and in research. These approaches are explored in depth in this chapter. The chapter starts with a summary of big data and its implementation in a number of fields, and then deals with the problems that big data presents and the need for other technology to resolve these issues/challenges. Big data can best be used with the aid of the machine learning model, even though they are not directly related. Thus, the paradigms of machine learning that support big data can be combined with big data technology, thus providing insight into a range of big data machine learning approaches and techniques. Although big data cannot rely solely on the few paradigms of machine learning, the underlying problems are addressed. New machine learning algorithms are needed that can explore the full scale of the big data process and enable software engineering firms to come up with better solutions.","PeriodicalId":324650,"journal":{"name":"Applications of Big Data in Large- and Small-Scale Systems","volume":"47 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applications of Big Data in Large- and Small-Scale Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-7998-6673-2.CH004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Big data and machine learning currently play an important role in various applications and in research. These approaches are explored in depth in this chapter. The chapter starts with a summary of big data and its implementation in a number of fields, and then deals with the problems that big data presents and the need for other technology to resolve these issues/challenges. Big data can best be used with the aid of the machine learning model, even though they are not directly related. Thus, the paradigms of machine learning that support big data can be combined with big data technology, thus providing insight into a range of big data machine learning approaches and techniques. Although big data cannot rely solely on the few paradigms of machine learning, the underlying problems are addressed. New machine learning algorithms are needed that can explore the full scale of the big data process and enable software engineering firms to come up with better solutions.
大数据机器学习
大数据和机器学习目前在各种应用和研究中发挥着重要作用。本章将深入探讨这些方法。本章首先概述了大数据及其在多个领域的实施,然后讨论了大数据所带来的问题以及解决这些问题/挑战所需的其他技术。大数据最好在机器学习模型的帮助下使用,即使它们没有直接关联。因此,支持大数据的机器学习范式可以与大数据技术相结合,从而提供对一系列大数据机器学习方法和技术的见解。虽然大数据不能仅仅依赖于机器学习的几个范例,但潜在的问题得到了解决。我们需要新的机器学习算法来全面探索大数据过程,并使软件工程公司能够提出更好的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信