TORUS 1 -- toward an open resource using services : cloud computing for environmental data /
| Other Authors: | |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Hoboken :
Wiley,
2020.
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| Subjects: | |
| Online Access: | Connect to the full text of this electronic book |
Table of 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