Algorithms for Sparsity-Constrained Optimization

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160,49 

Springer Theses 261

ISBN: 3319377191
ISBN 13: 9783319377193
Autor: Bahmani, Sohail
Verlag: Springer Verlag GmbH
Umfang: xxi, 107 S., 1 s/w Illustr., 12 farbige Illustr., 107 p. 13 illus., 12 illus. in color.
Erscheinungsdatum: 23.08.2016
Auflage: 1/2014
Produktform: Kartoniert
Einband: Kartoniert

This thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a“greedy“ algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many of the inaccuracies that occurred with the use of previous models.

Artikelnummer: 9678284 Kategorie:

Beschreibung

This thesis demonstrates techniques that provide faster and more accurate solutions to a variety of problems in machine learning and signal processing. The author proposes a "greedy" algorithm, deriving sparse solutions with guarantees of optimality. The use of this algorithm removes many of the inaccuracies that occurred with the use of previous models.

Autorenporträt

Dr. Bahmani completed his thesis at Carnegie Mellon University and is currently employed by the Georgia Institute of Technology.

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