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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." Full Product DetailsAuthor: Sohail BahmaniPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: Softcover reprint of the original 1st ed. 2014 Volume: 261 Dimensions: Width: 15.50cm , Height: 0.70cm , Length: 23.50cm Weight: 2.117kg ISBN: 9783319377193ISBN 10: 3319377191 Pages: 107 Publication Date: 23 August 2016 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsIntroduction.- 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.ReviewsAuthor InformationDr. Bahmani completed his thesis at Carnegie Mellon University and is currently employed by the Georgia Institute of Technology. Tab Content 6Author Website:Countries AvailableAll regions |
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