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Sparse modeling : theory, algorithms, and applications /

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개인저자Rish, Irina, 1969-, author.
Grabarnik, Genady Ya, author.
서명/저자사항Sparse modeling :theory, algorithms, and applications /Irina Rish, Genady Ya. Grabarnik.
형태사항1 online resource (xviii, 231 pages) : illustrations (some color).
총서사항Chapman & Hall/CRC machine learning & pattern recognition series
소장본 주기eBooks on EBSCOhostAll EBSCO eBooks
ISBN9781439828700
1439828709
1322667411
9781322667416
1439828695
9781439828694

서지주기Includes bibliographical references.
내용주기1. Introduction -- 2. Sparse recovery : problem formulations -- 3. Theoretical results (deterministic part) -- 4. Theoretical results (probabilistic part) -- 5. Algorithms for sparse recovery problems -- 6. Beyond LASSO : structured sparsity -- 7. Beyond LASSO : other loss functions -- 8. Sparse graphical models -- 9. Sparse matrix factorization : dictionary learning and beyond.
요약Sparse models are particularly useful in scientific applications, such as biomarker discovery in genetic or neuroimaging data, where the interpretability of a predictive model is essential. Sparsity can also dramatically improve the cost efficiency of signal processing. Sparse Modeling: Theory, Algorithms, and Applications provides an introduction to the growing field of sparse modeling, including application examples, problem formulations that yield sparse solutions, algorithms for finding such solutions, and recent theoretical results on sparse recovery.
일반주제명Mathematical models.
Sampling (Statistics)
Data reduction.
Sparse matrices.
SCIENCE -- System Theory.
TECHNOLOGY & ENGINEERING -- Operations Research.
Data reduction.
Mathematical models.
Sampling (Statistics)
Sparse matrices.
언어영어
기타형태 저록Print version:Rish, Irina, 1969-Sparse modeling.Boca Raton, FL : CRC Press : Taylor & Francis Group, 20159781439828694
대출바로가기http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=906006

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