A coherent introductory text from a groundbreaking researcher, focusing on clarity and motivation to build intuition and understanding.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Martin J. Wainwright is a Chancellor's Professor at the University of California, Berkeley, with a joint appointment between the Department of Statistics and the Department of Electrical Engineering and Computer Sciences. His research lies at the nexus of statistics, machine learning, optimization, and information theory, and he has published widely in all of these disciplines. He has written two other books, one on graphical models together with Michael I. Jordan, and one on sparse learning together with Trevor Hastie and Robert Tibshirani. Among other awards, he has received the COPSS Presdients' Award, has been a Medallion Lecturer and Blackwell Lecturer for the Institute of Mathematical Statistics, and has received Best Paper Awards from the Neural Information Processing Systems (NIPS), the International Conference on Machine Learning (ICML), and the Uncertainty in Artificial Intelligence (UAI) conferences, as well as from the Institute of Electrical and Electronics Engineers (IEEE) Information Theory Society.
Inhaltsangabe
1. Introduction 2. Basic tail and concentration bounds 3. Concentration of measure 4. Uniform laws of large numbers 5. Metric entropy and its uses 6. Random matrices and covariance estimation 7. Sparse linear models in high dimensions 8. Principal component analysis in high dimensions 9. Decomposability and restricted strong convexity 10. Matrix estimation with rank constraints 11. Graphical models for high-dimensional data 12. Reproducing kernel Hilbert spaces 13. Nonparametric least squares 14. Localization and uniform laws 15. Minimax lower bounds References Author index Subject index.
1. Introduction 2. Basic tail and concentration bounds 3. Concentration of measure 4. Uniform laws of large numbers 5. Metric entropy and its uses 6. Random matrices and covariance estimation 7. Sparse linear models in high dimensions 8. Principal component analysis in high dimensions 9. Decomposability and restricted strong convexity 10. Matrix estimation with rank constraints 11. Graphical models for high-dimensional data 12. Reproducing kernel Hilbert spaces 13. Nonparametric least squares 14. Localization and uniform laws 15. Minimax lower bounds References Author index Subject index.
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