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Beschreibung
Taking the Lasso method as its starting point, this book describes the main ingredients needed to study general loss functions and sparsity-inducing regularizers. It also provides a semi-parametric approach to establishing confidence intervals and tests. Sparsity-inducing methods have proven to be very useful in the analysis of high-dimensional data. Examples include the Lasso and group Lasso methods, and the least squares method with other norm-penalties, such as the nuclear norm. The illustrations provided include generalized linear models, density estimation, matrix completion and sparse principal components. Each chapter ends with a problem section. The book can be used as a textbook for a graduate or PhD course.
École d'Été de Probabilités de Saint-Flour XLV – 2015
Details
| Verlag | Springer International Publishing |
| Ersterscheinung | 29. Juni 2016 |
| Maße | 23.5 cm x 15.5 cm |
| Gewicht | 446 Gramm |
| Format | Softcover |
| ISBN-13 | 9783319327730 |
| Auflage | 1st ed. 2016 |
| Seiten | 274 |
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