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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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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New. Print on demand Contains: Illustrations, black & white, Illustrations, color. Springer Theses . XXI, 107 p. 13 illus., 12 illus. in color. Intended for professional and scholarly audience.
Add this copy of Algorithms for Sparsity-Constrained Optimization to cart. $209.58, new condition, Sold by Media Smart rated 4.0 out of 5 stars, ships from Hawthorne, CA, UNITED STATES, published 2013 by Springer.
