MARC details
| 000 -LEADER |
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| fixed length control field |
07245nam a22004693i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
EBC4860513 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MiAaPQ |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20191126093041.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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m o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr cnu|||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
191125s2017 xx o ||||0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781119229056 |
| Qualifying information |
(electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| Canceled/invalid ISBN |
9781119229049 |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(MiAaPQ)EBC4860513 |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(Au-PeEL)EBL4860513 |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(CaPaEBR)ebr11385347 |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(CaONFJC)MIL1011171 |
| 035 ## - SYSTEM CONTROL NUMBER |
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| System control number |
(OCoLC)974487431 |
| 040 ## - CATALOGING SOURCE |
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| Original cataloging agency |
MiAaPQ |
| Language of cataloging |
eng |
| Description conventions |
rda |
| -- |
pn |
| Transcribing agency |
MiAaPQ |
| Modifying agency |
MiAaPQ |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
TF241.A886 2017 |
| 082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
625.14028557 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Attoh-Okine, Nii. |
| 9 (RLIN) |
44287 |
| 245 10 - TITLE STATEMENT |
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| Title |
Big Data and Differential Privacy : |
| Remainder of title |
Analysis Strategies for Railway Track Engineering. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
New York : |
| Name of producer, publisher, distributor, manufacturer |
John Wiley & Sons, Incorporated, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2017. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Date of production, publication, distribution, manufacture, or copyright notice |
©2017. |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
1 online resource (270 pages) |
| 336 ## - CONTENT TYPE |
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| Content type term |
text |
| Content type code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
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| Media type term |
computer |
| Media type code |
c |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
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| Carrier type term |
online resource |
| Carrier type code |
cr |
| Source |
rdacarrier |
| 490 1# - SERIES STATEMENT |
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| Series statement |
Wiley Series in Operations Research and Management Science Ser. |
| 505 0# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Cover -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction -- 1.1 General -- 1.2 Track Components -- 1.3 Characteristics of Railway Track Data -- 1.4 Railway Track Engineering Problems -- 1.5 Wheel-Rail Interface Data -- 1.5.1 Switches and Crossings -- 1.6 Geometry Data -- 1.7 Track Geometry Degradation Models -- 1.7.1 Deterministic Models -- 1.7.1.1 Linear Models -- 1.7.1.2 Nonlinear Models -- 1.7.2 Stochastic Models -- 1.7.3 Discussion -- 1.8 Rail Defect Data -- 1.9 Inspection and Detection Systems -- 1.10 Rail Grinding -- 1.11 Traditional Data Analysis Techniques -- 1.11.1 Emerging Data Analysis -- 1.12 Remarks -- References -- Chapter 2 Data Analysis - Basic Overview -- 2.1 Introduction -- 2.2 Exploratory Data Analysis (EDA) -- 2.3 Symbolic Data Analysis -- 2.3.1 Building Symbolic Data -- 2.3.2 Advantages of Symbolic Data -- 2.4 Imputation -- 2.5 Bayesian Methods and Big Data Analysis -- 2.6 Remarks -- References -- Chapter 3 Machine Learning: A Basic Overview -- 3.1 Introduction -- 3.2 Supervised Learning -- 3.3 Unsupervised Learning -- 3.4 Semi-Supervised Learning -- 3.5 Reinforcement Learning -- 3.6 Data Integration -- 3.7 Data Science Ontology -- 3.7.1 Kernels -- 3.7.1.1 General -- 3.7.1.2 Learning Process -- 3.7.2 Basic Operations with Kernels -- 3.7.3 Different Kernel Types -- 3.7.4 Intuitive Example -- 3.7.5 Kernel Methods -- 3.7.5.1 Support Vector Machines -- 3.8 Imbalanced Classification -- 3.9 Model Validation -- 3.9.1 Receiver Operating Characteristic (ROC) Curves -- 3.9.1.1 ROC Curves -- 3.10 Ensemble Methods -- 3.10.1 General -- 3.10.2 Bagging -- 3.10.3 Boosting -- 3.11 Big P and Small N (P ≫ N) -- 3.11.1 Bias and Variances -- 3.11.2 Multivariate Adaptive Regression Splines (MARS) -- 3.12 Deep Learning -- 3.12.1 General -- 3.12.2 Deep Belief Networks. |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
3.12.2.1 Restricted Boltzmann Machines (RBM) -- 3.12.2.2 Deep Belief Nets (DBN) -- 3.12.3 Convolutional Neural Networks (CNN) -- 3.12.4 Granular Computing (Rough Set Theory) -- 3.12.5 Clustering -- 3.12.5.1 Measures of Similarity or Dissimilarity -- 3.12.5.2 Hierarchical Methods -- 3.12.5.3 Non-Hierarchical Clustering -- 3.12.5.4 k-Means Algorithm -- 3.12.5.5 Expectation-Maximization (EM) Algorithms -- 3.13 Data Stream Processing -- 3.13.1 Methods and Analysis -- 3.13.2 LogLog Counting -- 3.13.3 Count-Min Sketch -- 3.13.3.1 Online Support Regression -- 3.14 Remarks -- References -- Chapter 4 Basic Foundations of Big Data -- 4.1 Introduction -- 4.2 Query -- 4.3 Taxonomy of Big Data Analytics in Railway Track Engineering -- 4.4 Data Engineering -- 4.5 Remarks -- References -- Chapter 5 Hilbert-Huang Transform, Profile, Signal, and Image Analysis -- 5.1 Hilbert-Huang Transform -- 5.1.1 Traditional Empirical Mode Decomposition -- 5.1.1.1 Side Effect (Boundary Effect) -- 5.1.1.2 Example -- 5.1.1.3 Stopping Criterion -- 5.1.2 Ensemble Empirical Mode Decomposition (EEMD) -- 5.1.2.1 Post-Processing EEMD -- 5.1.3 Complex Empirical Mode Decomposition (CEMD) -- 5.1.4 Spectral Analysis -- 5.1.5 Bidimensional Empirical Mode Decomposition (BEMD) -- 5.1.5.1 Example -- 5.2 Axle Box Acceleration -- 5.2.1 General -- 5.3 Analysis -- 5.4 Remarks -- References -- Chapter 6 Tensors - Big Data in Multidimensional Settings -- 6.1 Introduction -- 6.2 Notations and Definitions -- 6.3 Tensor Decomposition Models -- 6.3.1 Nonnegative Tensor Factorization -- 6.4 Application -- 6.5 Remarks -- References -- Chapter 7 Copula Models -- 7.1 Introduction -- 7.1.1 Archimedean Copulas -- 7.1.1.1 Concordance Measures -- 7.1.2 Multivariate Archimedean Copulas -- 7.2 Pair Copula: Vines -- 7.3 Computational Example -- 7.3.1 Results -- 7.4 Remarks -- References. |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
Chapter 8 Topological Data Analysis -- 8.1 Introduction -- 8.2 Basic Ideas -- 8.2.1 Topology -- 8.2.2 Homology -- 8.2.2.1 Simplicial Complex -- 8.2.2.2 Cycles, Boundaries, and Homology -- 8.2.3 Persistent Homology -- 8.2.3.1 Filtration -- 8.2.4 Persistence Visualizations -- 8.2.4.1 Persistence Diagrams -- 8.3 A Simple Railway Track Engineering Application -- 8.3.1 Embedding Method -- 8.4 Remarks -- References -- Chapter 9 Bayesian Analysis -- 9.1 Introduction -- 9.1.1 Prior and Posterior Distributions -- 9.2 Markov Chain Monte Carlo (MCMC) -- 9.2.1 Gibbs Sampling -- 9.2.2 Metropolis-Hastings -- 9.3 Approximate Bayesian Computation -- 9.3.1 ABC - Rejection algorithm -- 9.3.2 ABC Steps -- 9.4 Markov Chain Monte Carlo Application -- 9.5 ABC Application -- 9.6 Remarks -- References -- Chapter 10 Basic Bayesian Nonparametrics -- 10.1 General -- 10.2 Dirichlet Family -- 10.2.1 Moments -- 10.2.1.1 Marginal Distribution -- 10.3 Dirichlet Process -- 10.3.1 Stick-Breaking Construction -- 10.3.2 Chinese Restaurant Process -- 10.3.3 Chinese Restaurant Process (CRP) for Infinite Mixture -- 10.3.4 Nonparametric Clustering and Dirichlet Process -- 10.4 Finite Mixture Modeling -- 10.5 Bayesian Nonparametric Railway Track -- 10.6 Remarks -- References -- Chapter 11 Basic Metaheuristics -- 11.1 Introduction -- 11.1.1 Particle Swarm Optimization -- 11.1.2 PSO Algorithm Parameters -- 11.2 Remarks -- References -- Chapter 12 Differential Privacy -- 12.1 General -- 12.2 Differential Privacy -- 12.2.1 Differential Privacy: Hypothetical Track Application -- 12.3 Remarks -- References -- Index -- EULA. |
| 588 ## - SOURCE OF DESCRIPTION NOTE |
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| Source of description note |
Description based on publisher supplied metadata and other sources. |
| 590 ## - LOCAL NOTE (RLIN) |
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| Local note |
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2019. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Railroad tracks--Mathematical models. |
| 9 (RLIN) |
44288 |
| 655 #4 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Electronic books. |
| 9 (RLIN) |
44289 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Print version: |
| Main entry heading |
Attoh-Okine, Nii |
| Title |
Big Data and Differential Privacy : Analysis Strategies for Railway Track Engineering |
| Place, publisher, and date of publication |
New York : John Wiley & Sons, Incorporated,c2017 |
| International Standard Book Number |
9781119229049 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) |
|---|
| Corporate name or jurisdiction name as entry element |
ProQuest (Firm) |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE |
|---|
| Uniform title |
Wiley Series in Operations Research and Management Science Ser. |
| 9 (RLIN) |
44290 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Uniform Resource Identifier |
<a href="https://ebookcentral.proquest.com/lib/thebc/detail.action?docID=4860513">https://ebookcentral.proquest.com/lib/thebc/detail.action?docID=4860513</a> |
| Public note |
Click to View |
| 887 ## - NON-MARC INFORMATION FIELD |
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| Content of non-MARC field |
EBK |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Ebrary |