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Regularization, optimization, kernels, and support vector machines / / edited by Johan A.K. Suykens, KU Leuven, Belgium, Marco Signoretto, KU Leuven, Belgium, Andreas Argyriou, Ecole Centrale Paris, France
| Regularization, optimization, kernels, and support vector machines / / edited by Johan A.K. Suykens, KU Leuven, Belgium, Marco Signoretto, KU Leuven, Belgium, Andreas Argyriou, Ecole Centrale Paris, France |
| Pubbl/distr/stampa | Boca Raton : , : Taylor & Francis, , [2015] |
| Descrizione fisica | 1 online resource (522 p.) |
| Disciplina |
511.8
511/.8 |
| Collana | Chapman and Hall/CRC Machine Learning & Pattern Recognition |
| Soggetto topico |
Mathematical models
Mathematical statistics |
| ISBN |
0-429-07612-6
1-4822-4140-4 |
| Classificazione | COM021030COM037000TEC007000 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Contents; Preface; Contributors; Chapter 1: An Equivalence between the Lasso and Support Vector Machines; Chapter 2: Regularized Dictionary Learning; Chapter 3: Hybrid Conditional Gradient-Smoothing Algorithms with Applications to Sparse and Low Rank Regularization; Chapter 4: Nonconvex Proximal Splitting with Computational Errors; Chapter 5: Learning Constrained Task Similarities in Graph-Regularized Multi-Task Learning; Chapter 6: The Graph-Guided Group Lasso for Genome-Wide Association Studies
Chapter 7: On the Convergence Rate of Stochastic Gradient Descent for Strongly Convex FunctionsChapter 8: Detecting Ineffective Features for Nonparametric Regression; Chapter 9: Quadratic Basis Pursuit; Chapter 10: Robust Compressive Sensing; Chapter 11: Regularized Robust Portfolio Estimation; Chapter 12: The Why and How of Nonnegative Matrix Factorization; Chapter 13: Rank Constrained Optimization Problems in Computer Vision; Chapter 14: Low-Rank Tensor Denoising and Recovery via Convex Optimization; Chapter 15: Learning Sets and Subspaces; Chapter 16: Output Kernel Learning Methods Chapter 17: Kernel-Based Identification of Systems with Multiple Outputs Using Nuclear Norm RegularizationChapter 18: Kernel Methods for Image Denoising; Chapter 19: Single-Source Domain Adaptation with Target and Conditional Shift; Chapter 20: Multi-Layer Support Vector Machines; Chapter 21: Online Regression with Kernels |
| Record Nr. | UNINA-9910787836703321 |
| Boca Raton : , : Taylor & Francis, , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Regularization, optimization, kernels, and support vector machines / / edited by Johan A.K. Suykens, KU Leuven, Belgium, Marco Signoretto, KU Leuven, Belgium, Andreas Argyriou, Ecole Centrale Paris, France
| Regularization, optimization, kernels, and support vector machines / / edited by Johan A.K. Suykens, KU Leuven, Belgium, Marco Signoretto, KU Leuven, Belgium, Andreas Argyriou, Ecole Centrale Paris, France |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Boca Raton : , : Taylor & Francis, , [2015] |
| Descrizione fisica | 1 online resource (522 p.) |
| Disciplina |
511.8
511/.8 |
| Collana | Chapman and Hall/CRC Machine Learning & Pattern Recognition |
| Soggetto topico |
Mathematical models
Mathematical statistics |
| ISBN |
0-429-07612-6
1-4822-4140-4 |
| Classificazione | COM021030COM037000TEC007000 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Front Cover; Contents; Preface; Contributors; Chapter 1: An Equivalence between the Lasso and Support Vector Machines; Chapter 2: Regularized Dictionary Learning; Chapter 3: Hybrid Conditional Gradient-Smoothing Algorithms with Applications to Sparse and Low Rank Regularization; Chapter 4: Nonconvex Proximal Splitting with Computational Errors; Chapter 5: Learning Constrained Task Similarities in Graph-Regularized Multi-Task Learning; Chapter 6: The Graph-Guided Group Lasso for Genome-Wide Association Studies
Chapter 7: On the Convergence Rate of Stochastic Gradient Descent for Strongly Convex FunctionsChapter 8: Detecting Ineffective Features for Nonparametric Regression; Chapter 9: Quadratic Basis Pursuit; Chapter 10: Robust Compressive Sensing; Chapter 11: Regularized Robust Portfolio Estimation; Chapter 12: The Why and How of Nonnegative Matrix Factorization; Chapter 13: Rank Constrained Optimization Problems in Computer Vision; Chapter 14: Low-Rank Tensor Denoising and Recovery via Convex Optimization; Chapter 15: Learning Sets and Subspaces; Chapter 16: Output Kernel Learning Methods Chapter 17: Kernel-Based Identification of Systems with Multiple Outputs Using Nuclear Norm RegularizationChapter 18: Kernel Methods for Image Denoising; Chapter 19: Single-Source Domain Adaptation with Target and Conditional Shift; Chapter 20: Multi-Layer Support Vector Machines; Chapter 21: Online Regression with Kernels |
| Record Nr. | UNINA-9910806147803321 |
| Boca Raton : , : Taylor & Francis, , [2015] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||