Bilde av Statistical Learning With Sparsity Av Trevor (stanford University Stanford University Stanford University California Usa Stanford University Californi

Statistical Learning With Sparsity Av Trevor (stanford University Stanford University Stanford University California Usa Stanford University Californi

Discover New Methods for Dealing with High-Dimensional Data A 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 linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recen

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Butikk	Produktnavn	Lagerstus	Pris
	Statistical Learning With Sparsity Av Trevor (stanford University Stanford University Stanford University California Usa Stanford University Californi	Se butikk	kr 499.00	Besøk butikk

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Statistical Learning With Sparsity Av Trevor (stanford University Stanford University Stanford University California Usa Stanford University Californi

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