نرمال‌سازی، بهینه‌سازی، هسته‌ها و ماشین‌های بردار پشتیبان ۲۰۱۴
Regularization, Optimization, Kernels, and Support Vector Machines 2014

1. Front Cover

2. Contents

3. Preface

4. Contributors

5. Chapter 1: An Equivalence between the Lasso and Support Vector Machines

6. Chapter 2: Regularized Dictionary Learning

7. Chapter 3: Hybrid Conditional Gradient-Smoothing Algorithms with Applications to Sparse and Low Rank Regularization

8. Chapter 4: Nonconvex Proximal Splitting with Computational Errors

9. Chapter 5: Learning Constrained Task Similarities in Graph-Regularized Multi-Task Learning

10. Chapter 6: The Graph-Guided Group Lasso for Genome-Wide Association Studies

11. Chapter 7: On the Convergence Rate of Stochastic Gradient Descent for Strongly Convex Functions

12. Chapter 8: Detecting Ineffective Features for Nonparametric Regression

13. Chapter 9: Quadratic Basis Pursuit

14. Chapter 10: Robust Compressive Sensing

15. Chapter 11: Regularized Robust Portfolio Estimation

16. Chapter 12: The Why and How of Nonnegative Matrix Factorization

17. Chapter 13: Rank Constrained Optimization Problems in Computer Vision

18. Chapter 14: Low-Rank Tensor Denoising and Recovery via Convex Optimization

19. Chapter 15: Learning Sets and Subspaces

20. Chapter 16: Output Kernel Learning Methods

21. Chapter 17: Kernel-Based Identification of Systems with Multiple Outputs Using Nuclear Norm Regularization

22. Chapter 18: Kernel Methods for Image Denoising

23. Chapter 19: Single-Source Domain Adaptation with Target and Conditional Shift

24. Chapter 20: Multi-Layer Support Vector Machines

25. Chapter 21: Online Regression with Kernels