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IV. Graph Approaches
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IV. Graph Approaches
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
11. Networks (1/4)
11. Networks (2/4)
11. Networks (3/4)
11. Networks (4/4)
12. Mining Large Graphs (1/6)
12. Mining Large Graphs (2/6)
12. Mining Large Graphs (3/6)
12. Mining Large Graphs (4/6)
12. Mining Large Graphs (5/6)
12. Mining Large Graphs (6/6)
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
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