國立虎尾科技大學 |

Machine learning for big data analyis

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learning for big data analyis/ edited by Siddhartha Bhattacharyya ... [et al.].
other author:	Bhattacharyya, Siddhartha,
Published:	Berlin ;De Gruyter, : c2019.,
Description:	1 online resource.
Subject:	Big data. -
Online resource:	https://www.degruyter.com/isbn/9783110551433
ISBN:	9783110551433

Machine learning for big data analyis
Machine learning for big data analyis[electronic resource] /edited by Siddhartha Bhattacharyya ... [et al.]. - Berlin ;De Gruyter,c2019. - 1 online resource. - De Gruyter frontiers in computational intelligence,v. 12512-8868 ;. - De Gruyter frontiers in computational intelligence,v. 1..

Includes bibliographical references and index.

This volume comprises six well-versed contributed chapters devoted to report the latest fi ndings on the applications of machine learning for big data analytics. Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. The possible challenges in this direction include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. Big data analytics is the process of examining large and varied data sets - i.e., big data - to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. This volume is intended to be used as a reference by undergraduate and post graduate students of the disciplines of computer science, electronics and telecommunication, information science and electrical engineering. 588

ISBN: 9783110551433

Standard No.: 10.1515/9783110551433doiSubjects--Topical Terms:

981821
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Machine learning for big data analyis
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520 $a This volume comprises six well-versed contributed chapters devoted to report the latest fi ndings on the applications of machine learning for big data analytics. Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. The possible challenges in this direction include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. Big data analytics is the process of examining large and varied data sets - i.e., big data - to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. This volume is intended to be used as a reference by undergraduate and post graduate students of the disciplines of computer science, electronics and telecommunication, information science and electrical engineering. 588 $a Description based on print version record.
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