Statistical Learning with Sparsity
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. They discuss the application of ℓ1 penalties to generalized l… Celý popis ↓
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Statistical Learning with Sparsity
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. 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. 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 also present statistical inference methods for
Parametre
- Výrobca
- Taylor & Francis Inc
- Autor
- Wainwright,Martin (Department of Statistics,Univer
- Nakladateľstvo
- Taylor & Francis
- Počet strán
- 367
- Rok vydania
- 2015
- ISBN
- 9781498712163
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