Central Library

Machine Learning for Big Data Analysis / (Record no. 84492)

000 -LEADER
fixed length control field 08641nam a22012015i 4500
001 - CONTROL NUMBER
control field 9783110551433
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240730161621.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 230228t20182019gw fo d z eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783110551433
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title
082 04 - CLASSIFICATION NUMBER
Call Number 005.7
245 00 - TITLE STATEMENT
Title Machine Learning for Big Data Analysis /
300 ## - PHYSICAL DESCRIPTION
Number of Pages 1 online resource (X, 183 p.)
490 0# - SERIES STATEMENT
Series statement De Gruyter Frontiers in Computational Intelligence ,
520 ## - SUMMARY, ETC.
Summary, etc 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. THE SERIES: FRONTIERS IN COMPUTATIONAL INTELLIGENCE The series Frontiers In Computational Intelligence is envisioned to provide comprehensive coverage and understanding of cutting edge research in computational intelligence. It intends to augment the scholarly discourse on all topics relating to the advances in artifi cial life and machine learning in the form of metaheuristics, approximate reasoning, and robotics. Latest research fi ndings are coupled with applications to varied domains of engineering and computer sciences. This field is steadily growing especially with the advent of novel machine learning algorithms being applied to different domains of engineering and technology. The series brings together leading researchers that intend to continue to advance the fi eld and create a broad knowledge about the most recent research.
700 1# - AUTHOR 2
Author 2 Adate, Amit,
700 1# - AUTHOR 2
Author 2 Bhattacharyya, Siddhartha,
700 1# - AUTHOR 2
Author 2 Bhattacharyya, Siddhartha,
700 1# - AUTHOR 2
Author 2 Bhaumik, Hrishikesh,
700 1# - AUTHOR 2
Author 2 Bhaumik, Hrishikesh,
700 1# - AUTHOR 2
Author 2 Bick, Markus,
700 1# - AUTHOR 2
Author 2 Blesik, Till,
700 1# - AUTHOR 2
Author 2 Chakraborty, Susanta,
700 1# - AUTHOR 2
Author 2 Choudhury, Abantika,
700 1# - AUTHOR 2
Author 2 De, Sourav,
700 1# - AUTHOR 2
Author 2 Deb, Moumita,
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Author 2 Gorbachev, S. V.,
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Author 2 Henesey, Lawrence,
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Author 2 Ho, Chiung Ching,
700 1# - AUTHOR 2
Author 2 Mishra, Deepak,
700 1# - AUTHOR 2
Author 2 Mukherjee, Anirban,
700 1# - AUTHOR 2
Author 2 Murawski, Matthias,
700 1# - AUTHOR 2
Author 2 Nirala, Satish,
700 1# - AUTHOR 2
Author 2 Ponraj, D. Narain,
700 1# - AUTHOR 2
Author 2 Sagayam, K. Martin,
700 1# - AUTHOR 2
Author 2 Sur, Surangam,
700 1# - AUTHOR 2
Author 2 Tripathy, B. K.,
700 1# - AUTHOR 2
Author 2 Vasanth, X. Ajay,
700 1# - AUTHOR 2
Author 2 Vurucu, Murat,
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://doi.org/10.1515/9783110551433
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://www.degruyter.com/isbn/9783110551433
856 42 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier https://www.degruyter.com/document/cover/isbn/9783110551433/original
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Koha item type eBooks
264 #1 -
-- Berlin ;
-- Boston :
-- De Gruyter,
-- [2018]
264 #4 -
-- ©2019
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-- text
-- txt
-- rdacontent
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-- computer
-- c
-- rdamedia
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-- online resource
-- cr
-- rdacarrier
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-- text file
-- PDF
-- rda
588 0# -
-- Description based on online resource; title from PDF title page (publisher's Web site, viewed 28. Feb 2023)
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Big data.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Machine learning.
650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Quantitative research.
650 #4 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Artificial Intelligence.
650 #4 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Machine Learning.
650 #4 - SUBJECT ADDED ENTRY--SUBJECT 1
-- Signal Processing.
650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1
-- COMPUTERS / Intelligence (AI) & Semantics.
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-- 978-3-11-060402-3 EBOOK PACKAGE Engineering, Computer Sciences 2018 English
-- 2018
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-- 978-3-11-060425-2 EBOOK PACKAGE COMPLETE 2018 English
-- 2018
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-- 978-3-11-061685-9 EBOOK PACKAGE COMPLETE DG 2019 English
-- 2019
912 ## -
-- 978-3-11-071956-7 DG Plus eBook-Package 2019
-- 2019
912 ## -
-- 978-3-11-076246-4 DG Plus DeG Package 2019 Part 1
-- 2019
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-- EBA_BACKALL
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-- EBA_CL_CHCOMSGSEN
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-- EBA_DGALL
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-- EBA_EBACKALL
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-- EBA_STMALL
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-- GBV-deGruyter-alles
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-- PDA12STME
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-- PDA13ENGE
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-- PDA18STMEE
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-- PDA5EBK
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-- ZDB-23-DEI
-- 2018
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-- ZDB-23-DGG
-- 2017

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