Handbook of big data
"Handbook of Big Data provides a state-of-the-art overview of the analysis of large-scale datasets. Featuring contributions from well-known experts in statistics and computer science, this handbook presents a carefully curated collection of techniques from both industry and academia. Thus, the...
| Otros Autores: | , , , |
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| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press
[2016]
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| Colección: | Chapman & Hall/CRC handbooks of modern statistical methods.
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| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009634729306719 |
Tabla de Contenidos:
- Front Cover; Contents; Preface; Editors; Contributors; I. General Perspectives on Big Data; 1. The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data; 2. Big-n versus Big-p in Big Data; II. Data-Centric, Exploratory Methods; 3. Divide and Recombine: Approach for Detailed Analysis and Visualization of Large Complex Data; 4. Integrate Big Data for Better Operation, Control, and Protection of Power Systems; 5. Interactive Visual Analysis of Big Data; 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator
- III. Efficient Algorithms7. High-Dimensional Computational Geometry; 8. IRLBA: Fast Partial Singular Value Decomposition Method; 9. Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms; 10. Something for (Almost) Nothing: New Advances in Sublinear-Time Algorithms; IV. Graph Approaches; 11. Networks; 12. Mining Large Graphs; V. Model Fitting and Regularization; 13. Estimator and Model Selection Using Cross-Validation; 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets; 15. Learning Structured Distributions
- 16. Penalized Estimation in Complex Models17. High-Dimensional Regression and Inference; VI. Ensemble Methods; 18. Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation; 19. Scalable Super Learning; VII. Causal Inference; 20. Tutorial for Causal Inference; 21. A Review of Some Recent Advances in Causal Inference; VIII. Targeted Learning; 22. Targeted Learning for Variable Importance; 23. Online Estimation of the Average Treatment Effect; 24. Mining with Inference: Data-Adaptive Target Parameters; Back Cover
