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Machine learning and big data : (Record no. 12736)

MARC details
000 -LEADER
fixed length control field 06755cam a22007218i 4500
001 - CONTROL NUMBER
control field on1193559419
003 - CONTROL NUMBER IDENTIFIER
control field OCoLC
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20240523125543.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m o d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr |||||||||||
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 200901t20202020nju ob 001 0 eng
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2020031698
040 ## - CATALOGING SOURCE
Original cataloging agency DLC
Language of cataloging eng
Description conventions rda
-- pn
Transcribing agency DLC
Modifying agency OCLCO
-- DG1
-- OCLCF
-- UKMGB
-- NLW
-- N$T
-- OCLCQ
-- OCLCO
-- OCLCQ
-- OCLCO
-- OCLCL
015 ## - NATIONAL BIBLIOGRAPHY NUMBER
National bibliography number GBC0G1245
Source bnb
019 ## -
-- 1198092075
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1119654793
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119654810
Qualifying information (adobe pdf)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1119654815
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119654834
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1119654831
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781119654797
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9781119654742
Qualifying information (hardback)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 1119654742
029 1# - OTHER SYSTEM CONTROL NUMBER (OCLC)
OCLC library identifier AU@
System control number 000067928970
029 1# - OTHER SYSTEM CONTROL NUMBER (OCLC)
OCLC library identifier AU@
System control number 000068856716
029 1# - OTHER SYSTEM CONTROL NUMBER (OCLC)
OCLC library identifier UKMGB
System control number 019979350
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1193559419
Canceled/invalid control number (OCoLC)1198092075
042 ## - AUTHENTICATION CODE
Authentication code pcc
050 00 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q325.5
082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3/1
Edition number 23
049 ## - LOCAL HOLDINGS (OCLC)
Holding library MAIN
245 00 - TITLE STATEMENT
Title Machine learning and big data :
Remainder of title concepts, algorithms, tools and applications /
Statement of responsibility, etc. edited by Uma N. Dulhare, Khaleel Ahmad, Khairol Amali Bin Ahmad.
263 ## - PROJECTED PUBLICATION DATE
Projected publication date 2010
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture Hoboken, NJ :
Name of producer, publisher, distributor, manufacturer Wiley-Scrivener,
Date of production, publication, distribution, manufacture, or copyright notice 2020.
264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Date of production, publication, distribution, manufacture, or copyright notice �2020
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource
336 ## - CONTENT TYPE
Content type term text
Content type code txt
Source rdacontent
337 ## - MEDIA TYPE
Media type term computer
Media type code c
Source rdamedia
338 ## - CARRIER TYPE
Carrier type term online resource
Carrier type code cr
Source rdacarrier
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references and index.
520 ## - SUMMARY, ETC.
Summary, etc. "Machine learning with big data technologies create new opportunities to understand the various data process related to medical or environmental aspects of agriculture. Machine learning as a field is now incredibly pervasive, with applications spanning from business intelligence to homeland security from analyzing biochemical interactions to structural monitoring of aging bridges, and from emissions to astrophysics, etc. As we are entering the Industrial Revolution 4.0, BD/ML applications, in combination with IoT/Cloud technologies, are fundamentally changing any domain-specific industry. Development of this field is very important because it can help to enhance human life by the automation system, which in turn has far-reaching effects for economic, psychological, educational and organizational improvements to the way we work, teach, learn and care for ourselves and each other"--
Assigning source Provided by publisher
588 0# - SOURCE OF DESCRIPTION NOTE
Source of description note Print version record and CIP data provided by publisher; resource not viewed.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Preface xix Section 1: Theoretical Fundamentals 1 1 Mathematical Foundation 3; Afroz and Basharat Hussain 1.1 Concept of Linear Algebra 3 1.1.1 Introduction 3 1.1.2 Vector Spaces 5 1.1.3 Linear Combination 6 1.1.4 Linearly Dependent and Independent Vectors 7 1.1.5 Linear Span, Basis and Subspace 8 1.1.6 Linear Transformation (or Linear Map) 9 1.1.7 Matrix Representation of Linear Transformation 10 1.1.8 Range and Null Space of Linear Transformation 13 1.1.9 Invertible Linear Transformation 15 1.2 Eigenvalues, Eigenvectors,
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note And Eigendecomposition of a Matrix 15 1.2.1 Characteristics Polynomial 16 1.2.1.1 Some Results on Eigenvalue 16 1.2.2 Eigendecomposition 18 1.3 Introduction to Calculus 20 1.3.1 Function 20 1.3.2 Limits of Functions 21 1.3.2.1 Some Properties of Limits 22 1.3.2.2 1nfinite Limits 25 1.3.2.3 Limits at Infinity 26 1.3.3 Continuous Functions and Discontinuous Functions 26 1.3.3.1 Discontinuous Functions 27 1.3.3.2 Properties of Continuous Function 27 1.3.4 Differentiation 28 References 29 2 Theory of Probability 31; Parvaze Ahmad Dar and Afroz 2.1 Introduction 31 2.1.1 Definition 31 2.1.1.1 Statistical Definition of Probability 31 2.1.1.2 Mathematical Definition of Probability 32 2.1.2 Some Basic Terms of Probability 32 2.1.2.1 Trial and Event 32 2.1.2.2 Exhaustive Events (Exhaustive Cases) 33 2.1.2.3 Mutually
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note One Variable Gaussian) 50 2.6.1.2 Degenerate Univariate Gaussian 51 2.6.1.3 Multivariate Gaussian 51 References 51 3 Correlation and Regression 53; Mohd. Abdul Haleem Rizwan 3.1 Introduction 53 3.2 Correlation 54 3.2.1 Positive Correlation and Negative Correlation 54 3.2.2 Simple Correlation and Multiple Correlation 54 3.2.3 Partial Correlation and Total Correlation 54 3.2.4 Correlation Coefficient 55 3.3 Regression 57 3.3.1 Linear Regression 64 3.3.2 Logistic Regression 64 3.3.3 Polynomial Regression 65 3.3.4 Stepwise Regression 66 3.3.5 Ridge Regression 67 3.3.6 Lasso Regression 67 3.3.7 Elastic Net Regression 68 3.4 Conclusion 68 References 69 Section 2: Big Data and Pattern Recognition 71 4 Data Preprocess 73; Md.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Of Things 121 5.6.11 Weather Forecasting 121 5.7 Where IoT Meets Big Data 122 5.7.1 IoT Platform 122 5.7.2 Sensors or Devices 123 5.7.3 Device Aggregators 123 5.7.4 IoT Gateway 123 5.7.5 Big Data Platform and Tools 124 5.8 Role of Machine Learning For Big Data and IoT 124 5.8.1 Typical Machine Learning Use Cases 125 5.9 Conclusion 126 References 127 6 Pattern Recognition Concepts 131; Ambeshwar Kumar, R. Manikandan and C.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Thaventhiran 6.1 Classifier 132 6.1.1 Introduction 132 6.1.2 Explanation-Based Learning 133 6.1.3 Isomorphism and Clique Method 135 6.1.4 Context-Dependent Classification 138 6.1.5 Summary 139 6.2 Feature Processing 140 6.2.1 Introduction 140 6.2.2 Detection and Extracting Edge With Boundary Line 141 6.2.3 Analyzing the Texture 142 6.2.4 Feature Mapping in Consecutive Moving Frame 143 6.2.5 Summary 145 6.3 Clustering 145 6.3.1 Introduction 145 6.3.2 Types of Clustering Algorithms 146 6.3.2.1 Dynamic Clustering Method 148 6.3.2.2 Model-Based Clustering 148 6.3.3 Application 149 6.3.4 Summary 150 6.4 Conclusion 151 References 151 Section 3: Machine Learning: Algorithms & Applications 153 7 Machine Learning 155; Elham Ghanbari and Sara Najafzadeh 7.1 History and Purpose of Machine Learning
590 ## - LOCAL NOTE (RLIN)
Local note John Wiley and Sons
Provenance (VM) [OBSOLETE] Wiley Online Library: Complete oBooks
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Big data.
650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Apprentissage automatique.
650 #6 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Donn�ees volumineuses.
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Big data
Source of heading or term fast
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning
Source of heading or term fast
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Dulhare, Uma N.,
Relator term editor.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Ahmad, Khaleel,
Relator term editor.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Khairol Amali Bin Ahmad,
Relator term editor.
758 ## - RESOURCE IDENTIFIER
Relationship information has work:
Label Machine learning and big data (Text)
Real World Object URI https://id.oclc.org/worldcat/entity/E39PCGT9tRxP4VQ9kcH8QKb7jK
Relationship https://id.oclc.org/worldcat/ontology/hasWork
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Print version:
Title Machine learning and big data.
Place, publisher, and date of publication Hoboken, NJ : Wiley-Scrivener, 2020
International Standard Book Number 9781119654742
Record control number (DLC) 2020031697
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="https://onlinelibrary.wiley.com/doi/book/10.1002/9781119654834">https://onlinelibrary.wiley.com/doi/book/10.1002/9781119654834</a>
938 ## -
-- EBSCOhost
-- EBSC
-- 2530916
994 ## -
-- 92
-- INLUM

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