ISBN: 9781498712163 - statistical learning with sparsity: the lasso and generalizations (22 risultati)

Condizione: Very Good. Very Good condition. A copy that may have a few cosmetic defects. May also contain light spine creasing or a few markings such as an owner's name, short gifter's inscription or light stamp.

Hardcover. Condizione: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority.

hardcover. Condizione: Good. . Satisfaction 100% guaranteed.

hardcover. Condizione: Good.

hardcover. Condizione: Good. 1st Edition. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Hardcover. Condizione: Very Good-. Condizione sovraccoperta: No Dust Jacket Issued. First Edition. Good+/very good- hardcover copy (NOT ex-library). Binding tight and sturdy. Previous owner's name on endpapers, a few pages with pencil markings. Binding is tight, sturdy, and square. Shelfwear is minimal. Ships same or next business day from Dinkytown in Minneapolis, Minnesota. Due to the size/weight of this book extra charges may apply for international shipping.…

hardcover. Condizione: As New.

Condizione: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.…

Condizione: New.

Condizione: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.…

hardcover. Condizione: New. 1st Edition. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Hardcover. Condizione: New. Brand new book, sourced directly from publisher. Dispatch time is 7-8 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condizione: As New. Unread book in perfect condition.

Condizione: New.

Condizione: New. 352.

Condizione: As New. Unread book in perfect condition.

Hardback. Condizione: New. New copy - Usually dispatched within 4 working days.

Condizione: New. 352.

Condizione: New. 352.

Condizione: New. Trevor Hastie is the John A. Overdeck Professor of Statistics at Stanford University. Prior to joining Stanford University, Professor Hastie worked at AT&ampT Bell Laboratories, where he helped develop the statistical modeling environme.

Buch. Condizione: Neu. Neuware - 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 1 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 recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.…

Hardcover. Condizione: new. New Copy. Customer Service Guaranteed.