MARC details
| 000 -LEADER |
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| fixed length control field |
04363cam a2200385 i 4500 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781119229070 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
1119229073 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781119229056 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
1119229057 |
| 040 ## - CATALOGING SOURCE |
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| Transcribing agency |
CUS |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Attoh-Okine, Nii O., |
| 245 10 - TITLE STATEMENT |
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| Title |
Big data and differential privacy : |
| Sub title |
analysis strategies for railway track engineering / |
| Statement of responsibility, etc. |
Nii O. Attoh-Okine. |
| 260 #1 - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Place of publication, distribution, etc. |
Hoboken, NJ : |
| Name of publisher, distributor, etc. |
John Wiley & Sons, Inc., |
| Date of publication, distribution, etc. |
2017. |
| 300 ## - DESCRIPTION |
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| Extent |
1 online resource (xiii, 252 pages). |
| 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. |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
1.12 RemarksReferences; 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. |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
3.7.2 Basic Operations with Kernels3.7.3 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 k 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; 3.12.2.1 Restricted Boltzmann Machines (RBM). |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
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. |
| 505 8# - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
4.3 Taxonomy of Big Data Analytics in Railway Track Engineering4.4 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. |
| 650 #0 - SUBJECT |
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| Keyword |
Railroad tracks |
| 650 #0 - SUBJECT |
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| Keyword |
Data protection |
| 650 #0 - SUBJECT |
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| Keyword |
Big data. |
| 650 #0 - SUBJECT |
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| Keyword |
Differential equations. |
| 650 #7 - SUBJECT |
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| Keyword |
TECHNOLOGY & ENGINEERING |
| 650 #7 - SUBJECT |
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| Keyword |
Big data. |
| 650 #7 - SUBJECT |
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| Keyword |
Data protection |
| 650 #7 - SUBJECT |
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| Keyword |
Differential equations. |
| 650 #7 - SUBJECT |
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| Keyword |
Railroad tracks |
| 856 40 - ONLINE RESOURCES |
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| url |
https://doi.org/10.1002/9781119229070 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Koha item type |
e-Books |
