18. Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation (4/4)
by Peter Bühlmann, Petros Drineas, Michael Kane, Mark van der Laan
Handbook of Big Data
- Front Cover (1/2)
- Front Cover (2/2)
- Contents
- Preface
- Editors
- Contributors
- I. General Perspectives on Big Data
- 1. The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data (1/4)
- 1. The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data (2/4)
- 1. The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data (3/4)
- 1. The Advent of Data Science: Some Considerations on the Unreasonable Effectiveness of Data (4/4)
- 2. Big-n versus Big-p in Big Data (1/3)
- 2. Big-n versus Big-p in Big Data (2/3)
- 2. Big-n versus Big-p in Big Data (3/3)
- II. Data-Centric, Exploratory Methods
- 3. Divide and Recombine: Approach for Detailed Analysis and Visualization of Large Complex Data (1/3)
- 3. Divide and Recombine: Approach for Detailed Analysis and Visualization of Large Complex Data (2/3)
- 3. Divide and Recombine: Approach for Detailed Analysis and Visualization of Large Complex Data (3/3)
- 4. Integrate Big Data for Better Operation, Control, and Protection of Power Systems (1/3)
- 4. Integrate Big Data for Better Operation, Control, and Protection of Power Systems (2/3)
- 4. Integrate Big Data for Better Operation, Control, and Protection of Power Systems (3/3)
- 5. Interactive Visual Analysis of Big Data (1/3)
- 5. Interactive Visual Analysis of Big Data (2/3)
- 5. Interactive Visual Analysis of Big Data (3/3)
- 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator (1/6)
- 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator (2/6)
- 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator (3/6)
- 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator (4/6)
- 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator (5/6)
- 6. A Visualization Tool for Mining Large Correlation Tables: The Association Navigator (6/6)
- III. Efficient Algorithms
- 7. High-Dimensional Computational Geometry (1/4)
- 7. High-Dimensional Computational Geometry (2/4)
- 7. High-Dimensional Computational Geometry (3/4)
- 7. High-Dimensional Computational Geometry (4/4)
- 8. IRLBA: Fast Partial Singular Value Decomposition Method (1/3)
- 8. IRLBA: Fast Partial Singular Value Decomposition Method (2/3)
- 8. IRLBA: Fast Partial Singular Value Decomposition Method (3/3)
- 9. Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms (1/4)
- 9. Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms (2/4)
- 9. Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms (3/4)
- 9. Structural Properties Underlying High-Quality Randomized Numerical Linear Algebra Algorithms (4/4)
- 10. Something for (Almost) Nothing: New Advances in Sublinear-Time Algorithms (1/3)
- 10. Something for (Almost) Nothing: New Advances in Sublinear-Time Algorithms (2/3)
- 10. Something for (Almost) Nothing: New Advances in Sublinear-Time Algorithms (3/3)
- IV. Graph Approaches
- V. Model Fitting and Regularization
- 13. Estimator and Model Selection Using Cross-Validation (1/4)
- 13. Estimator and Model Selection Using Cross-Validation (2/4)
- 13. Estimator and Model Selection Using Cross-Validation (3/4)
- 13. Estimator and Model Selection Using Cross-Validation (4/4)
- 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets (1/6)
- 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets (2/6)
- 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets (3/6)
- 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets (4/6)
- 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets (5/6)
- 14. Stochastic Gradient Methods for Principled Estimation with Large Datasets (6/6)
- 15. Learning Structured Distributions (1/4)
- 15. Learning Structured Distributions (2/4)
- 15. Learning Structured Distributions (3/4)
- 15. Learning Structured Distributions (4/4)
- 16. Penalized Estimation in Complex Models (1/4)
- 16. Penalized Estimation in Complex Models (2/4)
- 16. Penalized Estimation in Complex Models (3/4)
- 16. Penalized Estimation in Complex Models (4/4)
- 17. High-Dimensional Regression and Inference (1/4)
- 17. High-Dimensional Regression and Inference (2/4)
- 17. High-Dimensional Regression and Inference (3/4)
- 17. High-Dimensional Regression and Inference (4/4)
- VI. Ensemble Methods
- 18. Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation (1/4)
- 18. Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation (2/4)
- 18. Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation (3/4)
- 18. Divide and Recombine: Subsemble, Exploiting the Power of Cross-Validation (4/4)
- 19. Scalable Super Learning (1/4)
- 19. Scalable Super Learning (2/4)
- 19. Scalable Super Learning (3/4)
- 19. Scalable Super Learning (4/4)
- VII. Causal Inference
- 20. Tutorial for Causal Inference (1/6)
- 20. Tutorial for Causal Inference (2/6)
- 20. Tutorial for Causal Inference (3/6)
- 20. Tutorial for Causal Inference (4/6)
- 20. Tutorial for Causal Inference (5/6)
- 20. Tutorial for Causal Inference (6/6)
- 21. A Review of Some Recent Advances in Causal Inference (1/5)
- 21. A Review of Some Recent Advances in Causal Inference (2/5)
- 21. A Review of Some Recent Advances in Causal Inference (3/5)
- 21. A Review of Some Recent Advances in Causal Inference (4/5)
- 21. A Review of Some Recent Advances in Causal Inference (5/5)
- VIII. Targeted Learning
- 22. Targeted Learning for Variable Importance (1/4)
- 22. Targeted Learning for Variable Importance (2/4)
- 22. Targeted Learning for Variable Importance (3/4)
- 22. Targeted Learning for Variable Importance (4/4)
- 23. Online Estimation of the Average Treatment Effect (1/2)
- 23. Online Estimation of the Average Treatment Effect (2/2)
- 24. Mining with Inference: Data-Adaptive Target Parameters (1/3)
- 24. Mining with Inference: Data-Adaptive Target Parameters (2/3)
- 24. Mining with Inference: Data-Adaptive Target Parameters (3/3)
- Back Cover
338 Handb ook of Big Data
14. Mark J. van der Laan, Eric C. Polley, and Alan E. Hubbard. Super learner. Statistical
Applications in Genetics and Molecular Biology, 6(1):25, 2007.
15. David H. Wolpert. Stacked generalization. Neural Networks, 5(2):241–259, 1992.
16. Yuchen Zhang, John C. Duchi, and Martin J. Wainwright. Communication-efficient
algorithms for statistical optimization. Journal of Machine Learning Research,
14(1):3321–3363, 2013.
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