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000783677 010__ $$a  2017010092
000783677 020__ $$a9781119229070$$q(electronic book)
000783677 020__ $$a1119229073$$q(electronic book)
000783677 020__ $$a9781119229056$$q(electronic book)
000783677 020__ $$a1119229057$$q(electronic book)
000783677 020__ $$z9781119229063
000783677 020__ $$z1119229065
000783677 020__ $$z9781119229049
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000783677 035__ $$a(OCoLC)974487431
000783677 040__ $$aMiAaPQ$$beng$$erda$$epn$$cMiAaPQ$$dMiAaPQ
000783677 050_4 $$aTF241$$b.A88 2017
000783677 08200 $$a625.1/4028557$$223
000783677 1001_ $$aAttoh-Okine, Nii O.,$$eauthor.
000783677 24510 $$aBig data and differential privacy :$$banalysis strategies for railway track engineering /$$cNii O. Attoh-Okine.
000783677 264_1 $$aHoboken, NJ :$$bJohn Wiley & Sons, Inc.,$$c2017.
000783677 300__ $$a1 online resource (xiii, 252 pages.)
000783677 336__ $$atext$$btxt$$2rdacontent
000783677 337__ $$acomputer$$bc$$2rdamedia
000783677 338__ $$aonline resource$$bcr$$2rdacarrier
000783677 4901_ $$aWiley series in operations research and management science
000783677 504__ $$aIncludes bibliographical references and index.
000783677 5050_ $$aCover; 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
000783677 5058_ $$a1.12 RemarksReferences; Chapter 2 Data Analysis -- Basic Overview; 2.1 Introduction; 2.2 Exploratory Data Analysis (EDA); 2.3 Symbolic Data Analysis; 2.3.1 Building Symbolic Data; 2.3.2 Advantages of Symbolic Data; 2.4 Imputation; 2.5 Bayesian Methods and Big Data Analysis; 2.6 Remarks; References; Chapter 3 Machine Learning: A Basic Overview; 3.1 Introduction; 3.2 Supervised Learning; 3.3 Unsupervised Learning; 3.4 Semi-Supervised Learning; 3.5 Reinforcement Learning; 3.6 Data Integration; 3.7 Data Science Ontology; 3.7.1 Kernels; 3.7.1.1 General; 3.7.1.2 Learning Process
000783677 5058_ $$a3.7.2 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)
000783677 5058_ $$a3.12.2.2 Deep Belief Nets (DBN)3.12.3 Convolutional Neural Networks (CNN); 3.12.4 Granular Computing (Rough Set Theory); 3.12.5 Clustering; 3.12.5.1 Measures of Similarity or Dissimilarity; 3.12.5.2 Hierarchical Methods; 3.12.5.3 Non-Hierarchical Clustering; 3.12.5.4 k-Means Algorithm; 3.12.5.5 Expectation-Maximization (EM) Algorithms; 3.13 Data Stream Processing; 3.13.1 Methods and Analysis; 3.13.2 LogLog Counting; 3.13.3 Count-Min Sketch; 3.13.3.1 Online Support Regression; 3.14 Remarks; References; Chapter 4 Basic Foundations of Big Data; 4.1 Introduction; 4.2 Query
000783677 5058_ $$a4.3 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
000783677 506__ $$aAccess limited to authorized users
000783677 5880_ $$aPrint version record and CIP data provided by publisher; resource not viewed.
000783677 650_0 $$aRailroad tracks$$xMathematical models.
000783677 650_0 $$aData protection$$xMathematics.
000783677 650_0 $$aBig data.
000783677 650_0 $$aDifferential equations.
000783677 77608 $$iPrint version:$$aAttoh-Okine, Nii O.$$tBig data and differential privacy : analysis strategies for railway track engineering.$$dHoboken, New Jersey : Wiley, c2017$$z9781119229049$$w2017005398
000783677 830_0 $$aWiley series in operations research and management science.
000783677 85280 $$bebk$$hProQuest Ebook Central Academic Complete
000783677 85640 $$3ProQuest Ebook Central Academic Complete$$uhttps://univsouthin.idm.oclc.org/login?url=http://ebookcentral.proquest.com/lib/usiricelib-ebooks/detail.action?docID=4860513$$zOnline Access
000783677 909CO $$ooai:library.usi.edu:783677$$pGLOBAL_SET
000783677 980__ $$aEBOOK
000783677 980__ $$aBIB
000783677 982__ $$aEbook
000783677 983__ $$aOnline