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Algorithms for Sparsity-Constrained Optimization
Overview
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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Details
- ISBN-13: 9783319377193
- ISBN-10: 3319377191
- Publisher: Springer
- Publish Date: August 2016
- Dimensions: 9.21 x 6.14 x 0.28 inches
- Shipping Weight: 0.43 pounds
- Page Count: 107
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