Algorithms for Sparsity-Constrained Optimization için kapak resmi
Başlık:
Algorithms for Sparsity-Constrained Optimization
Dil:
English
ISBN:
9783319018812
Yayın Bilgileri:
Cham : Springer International Publishing : Imprint: Springer, 2014.
Fiziksel Tanımlama:
XXI, 107 p. 13 illus., 12 illus. in color. online resource.
Seri:
Springer Theses, Recognizing Outstanding Ph.D. Research, 261
İçerik:
Introduction -- Preliminaries -- Sparsity-Constrained Optimization -- Background -- 1-bit Compressed Sensing -- Estimation Under Model-Based Sparsity -- Projected Gradient Descent for `p-constrained Least Squares -- Conclusion and Future Work.
Özet:
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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E-Kitap 1822419-1001 TK5102.9 SPRINGER E-Kitap Koleksiyonu
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Özet

Özet

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.


Yazar Notları

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