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02459nam a22005295i 4500 |
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978-3-319-01881-2 |
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DE-He213 |
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20151028111648.0 |
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cr nn 008mamaa |
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131007s2014 gw | s |||| 0|eng d |
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|a 9783319018812
|9 978-3-319-01881-2
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|a 10.1007/978-3-319-01881-2
|2 doi
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|a 621.382
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|a Bahmani, Sohail.
|e author.
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|a Algorithms for Sparsity-Constrained Optimization
|h [electronic resource] /
|c by Sohail Bahmani.
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|a Cham :
|b Springer International Publishing :
|b Imprint: Springer,
|c 2014.
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|a XXI, 107 p. 13 illus., 12 illus. in color.
|b online resource.
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|a text
|b txt
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|a computer
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|a online resource
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|a text file
|b PDF
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|a Springer Theses, Recognizing Outstanding Ph.D. Research,
|x 2190-5053 ;
|v 261
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|a 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.
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|a 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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|a Engineering.
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|a Image processing.
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|a Computer science
|x Mathematics.
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|a Computer mathematics.
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|a Engineering.
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|a Signal, Image and Speech Processing.
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|a Mathematical Applications in Computer Science.
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|a Image Processing and Computer Vision.
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|a SpringerLink (Online service)
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|t Springer eBooks
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|i Printed edition:
|z 9783319018805
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|a Springer Theses, Recognizing Outstanding Ph.D. Research,
|x 2190-5053 ;
|v 261
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|u http://dx.doi.org/10.1007/978-3-319-01881-2
|z Full Text via HEAL-Link
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|a ZDB-2-ENG
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|a Engineering (Springer-11647)
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