Statistical learning with sparsity: the lasso and generalizations

Medientyp: Buch

Titel: Statistical learning with sparsity : the lasso and generalizations

Beteiligte: Hastie, Trevor [VerfasserIn]; Tibshirani, Robert [VerfasserIn]; Wainwright, Martin J. [VerfasserIn]

Erschienen: Boca Raton [u.a.]: CRC Press, [2015]

Umfang: xv, 351 Seiten; Illustrationen, Diagramme

Sprache: Englisch

ISBN: 9781498712163

RVK-Notation: SK 850 : Angewandte Statistik, Tabellen
CM 4000 : Statistik
SK 840 : Spezielle statistische Verfahren
ST 600 : Mathematik, Statistik

Schlagwörter: Statistik > Maschinelles Lernen

Entstehung:

Anmerkungen: Literaturverz. S. 315 - 335

Beschreibung: Front Cover; Contents; Preface; Chapter 1: Introduction; Chapter 2: The Lasso for Linear Models; Chapter 3: Generalized Linear Models; Chapter 4: Generalizations of the Lasso Penalty; Chapter 5: Optimization Methods; Chapter 6: Statistical Inference; Chapter 7: Matrix Decompositions, Approximations, and Completion; Chapter 8: Sparse Multivariate Methods; Chapter 9: Graphs and Model Selection; Chapter 10: Signal Approximation and Compressed Sensing; Chapter 11: Theoretical Results for the Lasso; Bibliography; Back Cover

Discover New Methods for Dealing with High-Dimensional DataA sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of ℓ1 penalties to generalized l

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0 : Monographs on statistics and applied probability

Statistical learning with sparsity : the lasso and generalizations

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