Electronic Resource

Algorithms for Sparsity-Constrained Optimization

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Penilaian

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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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
1408000504	EB0001603	519.6 Bah a	Central Library (OPAC)	Available

Detail Information

Series Title

Springer Theses

Call Number

519.6 Bah a

Publisher

Switzerland : Springer Cham., 2013

Collation

xxi, 107p.: Ill.

Language

English

ISBN/ISSN

978-3-319-01881-2

Classification

519.6

Content Type

-

Media Type

-

Carrier Type

-

Edition

1

Subject(s)

Mathematical optimization

Specific Detail Info

-

Statement of Responsibility

RIZAL

Other version/related

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