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Algorithms for Sparsity-Constrained Optimization
Sohail Bahmani
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Description for Algorithms for Sparsity-Constrained Optimization
Paperback. This thesis presents a wholly new technique in the structural analysis of data that uses a 'greedy' algorithm to derive optimal sparse solutions, enabling faster and more accurate results in formerly problematic areas of machine learning and signal processing. Series: Springer Theses. Num Pages: 128 pages, 1 black & white illustrations, 12 colour illustrations, 2 colour tables, biography. BIC Classification: PBWH; TTBM; UYT. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 7. Weight in Grams: 215.
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.
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.
Product Details
Format
Paperback
Publication date
2016
Publisher
Springer International Publishing AG Switzerland
Number of pages
128
Condition
New
Series
Springer Theses
Number of Pages
107
Place of Publication
Cham, Switzerland
ISBN
9783319377193
SKU
V9783319377193
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
About Sohail Bahmani
Dr. Bahmani completed his thesis at Carnegie Mellon University and is currently employed by the Georgia Institute of Technology.
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