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Sparse modeling : theory, algorithms, and applications /
Sparse 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, Algo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press,
[2015]
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| Colección: | Chapman & Hall/CRC machine learning & pattern recognition series.
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Rish, Irina, |d 1969- |e author. | |
| 245 | 1 | 0 | |a Sparse modeling : |b theory, algorithms, and applications / |c Irina Rish, Genady Ya. Grabarnik. |
| 264 | 1 | |a Boca Raton, FL : |b CRC Press, |c [2015] | |
| 264 | 4 | |c ©2015 | |
| 300 | |a 1 online resource (xviii, 231 pages) : |b illustrations (some color) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a data file | ||
| 490 | 1 | |a Chapman & Hall/CRC machine learning & pattern recognition series | |
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a 1. 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. | |
| 588 | 0 | |a Online resource; title from PDF title page (EBSCO; viewed on December 5, 2014). | |
| 520 | |a Sparse 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. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Mathematical models. | |
| 650 | 0 | |a Sampling (Statistics) | |
| 650 | 0 | |a Data reduction. | |
| 650 | 0 | |a Sparse matrices. | |
| 650 | 6 | |a Modèles mathématiques. | |
| 650 | 6 | |a Échantillonnage (Statistique) | |
| 650 | 6 | |a Réduction des données (Statistique) | |
| 650 | 6 | |a Matrices éparses. | |
| 650 | 7 | |a mathematical models. |2 aat | |
| 650 | 7 | |a SCIENCE |x System Theory. |2 bisacsh | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING |x Operations Research. |2 bisacsh | |
| 650 | 7 | |a Data reduction. |2 fast |0 (OCoLC)fst00887976 | |
| 650 | 7 | |a Mathematical models. |2 fast |0 (OCoLC)fst01012085 | |
| 650 | 7 | |a Sampling (Statistics) |2 fast |0 (OCoLC)fst01104676 | |
| 650 | 7 | |a Sparse matrices. |2 fast |0 (OCoLC)fst01128743 | |
| 700 | 1 | |a Grabarnik, Genady Ya., |e author. | |
| 776 | 0 | 8 | |i Print version: |a Rish, Irina, 1969- |t Sparse modeling. |d Boca Raton, FL : CRC Press : Taylor & Francis Group, 2015 |z 9781439828694 |w (OCoLC)902837893 |
| 830 | 0 | |a Chapman & Hall/CRC machine learning & pattern recognition series. | |
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