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TORUS 1 -- toward an open resource using services [electronic resource] : cloud computing for environmental data / edited by Dominique Laffly.

Contributor(s): Laffly, Dominique.
Material type: materialTypeLabelBookPublisher: Hoboken : Wiley, 2020Description: 1 online resource (345 p.).ISBN: 9781119720492; 1119720494; 9781119720478; 1119720478.Subject(s): Cloud computing | Open source software | Cloud computing | Open source softwareGenre/Form: Electronic books. | Electronic books.Additional physical formats: Print version:: TORUS 1 - Toward an Open Resource Using Services : Cloud Computing for Environmental DataDDC classification: 004.67/82 Online resources: Wiley Online Library
Contents:
Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface: Why TORUS? Toward an Open Resource Using Services, or How to Bring Environmental Science Closer to Cloud Computing -- Structure of the book -- PART 1: Integrated Analysis in Geography: The Way to Cloud Computing -- Introduction to Part 1 -- Introduction: the landscape as a system -- 1. Geographical Information and Landscape, Elements of Formalization -- 2. Sampling Strategies -- 2.1. References -- 3. Characterization of the Spatial Structure -- 4. Thematic Information Structures
5. From the Point to the Surface, How to Link Endogenous and Exogenous Data -- 5.1. References -- 6. Big Data in Geography -- Conclusion to Part 1: Why Here But Not There? -- PART 2: Basic Mathematical, Statistical and Computational Tools -- 7. An Introduction to Machine Learning -- 7.1. Predictive modeling: introduction -- 7.2. Bayesian modeling -- 7.2.1. Basic probability theory -- 7.2.2. Bayes rule -- 7.2.3. Parameter estimation -- 7.2.4. Learning Gaussians -- 7.3. Generative versus discriminative models -- 7.4. Classification -- 7.4.1. Naïve Bayes -- 7.4.2. Support vector machines
7.5. Evaluation metrics for classification evaluation -- 7.5.1. Confusion matrix-based measures -- 7.5.2. Area under the ROC curve (AUC) -- 7.6. Cross-validation and over-fitting -- 7.7. References -- 8. Multivariate Data Analysis -- 8.1. Introduction -- 8.2. Principal component analysis -- 8.2.1. How to measure the information -- 8.2.2. Scalar product and orthogonal variables -- 8.2.3. Construction of the principal axes -- 8.2.4. Analysis of the principal axes -- 8.2.5. Analysis of the data points -- 8.3. Multiple correspondence analysis -- 8.3.1. Indicator matrix -- 8.3.2. Cloud of data points
8.3.3. Cloud of levels -- 8.3.4. MCA or PCA? -- 8.4. Clustering -- 8.4.1. Distance between data points -- 8.4.2. Dissimilarity criteria between clusters -- 8.4.3. Variance (inertia) decomposition -- 8.4.4. k-means method -- 8.4.5. Agglomerative hierarchical clustering -- 8.5. References -- 9. Sensitivity Analysis -- 9.1. Generalities -- 9.2. Methods based on linear regression -- 9.2.1. Presentation -- 9.2.2. R practice -- 9.3. Morris' method -- 9.3.1. Elementary effects method (Morris' method) -- 9.3.2. R practice -- 9.4. Methods based on variance analysis -- 9.4.1. Sobol' indices
9.4.2. Estimation of the Sobol' indices -- 9.4.3. R practice -- 9.5. Conclusion -- 9.6. References -- 10. Using R for Multivariate Analysis -- 10.1. Introduction -- 10.1.1. The dataset -- 10.1.2. The variables -- 10.2. Principal component analysis -- 10.2.1. Eigenvalues -- 10.2.2. Data points (Individuals) -- 10.2.3. Supplementary variables -- 10.2.4. Other representations -- 10.3. Multiple correspondence analysis -- 10.4. Clustering -- 10.4.1. k-means algorithm -- 10.5. References -- PART 3: Computer Science -- 11. High Performance and Distributed Computing -- 11.1. High performance computing
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Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface: Why TORUS? Toward an Open Resource Using Services, or How to Bring Environmental Science Closer to Cloud Computing -- Structure of the book -- PART 1: Integrated Analysis in Geography: The Way to Cloud Computing -- Introduction to Part 1 -- Introduction: the landscape as a system -- 1. Geographical Information and Landscape, Elements of Formalization -- 2. Sampling Strategies -- 2.1. References -- 3. Characterization of the Spatial Structure -- 4. Thematic Information Structures

5. From the Point to the Surface, How to Link Endogenous and Exogenous Data -- 5.1. References -- 6. Big Data in Geography -- Conclusion to Part 1: Why Here But Not There? -- PART 2: Basic Mathematical, Statistical and Computational Tools -- 7. An Introduction to Machine Learning -- 7.1. Predictive modeling: introduction -- 7.2. Bayesian modeling -- 7.2.1. Basic probability theory -- 7.2.2. Bayes rule -- 7.2.3. Parameter estimation -- 7.2.4. Learning Gaussians -- 7.3. Generative versus discriminative models -- 7.4. Classification -- 7.4.1. Naïve Bayes -- 7.4.2. Support vector machines

7.5. Evaluation metrics for classification evaluation -- 7.5.1. Confusion matrix-based measures -- 7.5.2. Area under the ROC curve (AUC) -- 7.6. Cross-validation and over-fitting -- 7.7. References -- 8. Multivariate Data Analysis -- 8.1. Introduction -- 8.2. Principal component analysis -- 8.2.1. How to measure the information -- 8.2.2. Scalar product and orthogonal variables -- 8.2.3. Construction of the principal axes -- 8.2.4. Analysis of the principal axes -- 8.2.5. Analysis of the data points -- 8.3. Multiple correspondence analysis -- 8.3.1. Indicator matrix -- 8.3.2. Cloud of data points

8.3.3. Cloud of levels -- 8.3.4. MCA or PCA? -- 8.4. Clustering -- 8.4.1. Distance between data points -- 8.4.2. Dissimilarity criteria between clusters -- 8.4.3. Variance (inertia) decomposition -- 8.4.4. k-means method -- 8.4.5. Agglomerative hierarchical clustering -- 8.5. References -- 9. Sensitivity Analysis -- 9.1. Generalities -- 9.2. Methods based on linear regression -- 9.2.1. Presentation -- 9.2.2. R practice -- 9.3. Morris' method -- 9.3.1. Elementary effects method (Morris' method) -- 9.3.2. R practice -- 9.4. Methods based on variance analysis -- 9.4.1. Sobol' indices

9.4.2. Estimation of the Sobol' indices -- 9.4.3. R practice -- 9.5. Conclusion -- 9.6. References -- 10. Using R for Multivariate Analysis -- 10.1. Introduction -- 10.1.1. The dataset -- 10.1.2. The variables -- 10.2. Principal component analysis -- 10.2.1. Eigenvalues -- 10.2.2. Data points (Individuals) -- 10.2.3. Supplementary variables -- 10.2.4. Other representations -- 10.3. Multiple correspondence analysis -- 10.4. Clustering -- 10.4.1. k-means algorithm -- 10.5. References -- PART 3: Computer Science -- 11. High Performance and Distributed Computing -- 11.1. High performance computing

11.2. Systems based on multi-core CPUs

Includes bibliographical references and index.

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