[PDF.25hf] Practical Applications of Sparse Modeling (Neural Information Processing series)

Practical Applications of Sparse Modeling (Neural Information Processing series)

[PDF.yx45] Practical Applications of Sparse Modeling (Neural Information Processing series)

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 | #1894653 in Books |  2014-09-12 | Original language:English | PDF # 1 |  10.00 x.56 x8.00l,.0 | File type: PDF | 264 pages

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||About the Author||Irina Rish, Guillermo Cecchi, and Aurelie Lozao are Research Staff Members at IBM T. J. Watson Research Center, New York. Alexandru Niculescu-Mizil is a Researcher at the Machine Learning Department at NEC Labs America, Princeton, New Jersey.

Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse modeling, focusing on its applications in fields including neuroscience, computational biology, and computer vision.

Sparse modeling methods can improve the interpretability o...

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