Neural Information Processing series: Practical Applications of Sparse Modeling (PDF)
(Sprache: Englisch)
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...
Leider schon ausverkauft
eBook
79.88 €
39 DeutschlandCard Punkte sammeln
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenloser tolino webreader
Produktdetails
Produktinformationen zu „Neural Information Processing series: Practical Applications of Sparse Modeling (PDF)“
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 of predictive models and aid efficient recovery of high-dimensional unobserved signals from a limited number of measurements. Yet despite significant advances in the field, a number of open issues remain when sparse modeling meets real-life applications. The book discusses a range of practical applications and state-of-the-art approaches for tackling the challenges presented by these applications. Topics considered include the choice of method in genomics applications; analysis of protein mass-spectrometry data; the stability of sparse models in brain imaging applications; sequential testing approaches; algorithmic aspects of sparse recovery; and learning sparse latent models.ContributorsA. Vania Apkarian, Marwan Baliki, Melissa K. Carroll, Guillermo A. Cecchi, Volkan Cevher, Xi Chen, Nathan W. Churchill, Remi Emonet, Rahul Garg, Zoubin Ghahramani, Lars Kai Hansen, Matthias Hein, Katherine Heller, Sina Jafarpour, Seyoung Kim, Mladen Kolar, Anastasios Kyrillidis, Seunghak Lee, Aurelie Lozano, Matthew L. Malloy, Pablo Meyer, Shakir Mohamed, Alexandru Niculescu-Mizil, Robert D. Nowak, Jean-Marc Odobez, Peter M. Rasmussen, Irina Rish, Saharon Rosset, Martin Slawski, Stephen C. Strother, Jagannadan Varadarajan, Eric P. Xing
Bibliographische Angaben
- Autoren: Guillermo A. Cecchi , Alexandru Niculescu-Mizil , Aurelie Lozano , Irina Rish
- 2014, 264 Seiten, Englisch
- Herausgegeben: Guillermo A. Cecchi, Alexandru Niculescu-Mizil, Aurelie Lozano, Irina Rish, Michael I. Jordan, Thomas G. Dietterich
- ISBN-10: 0262325330
- ISBN-13: 9780262325332
- Erscheinungsdatum: 19.09.2014
Abhängig von Bildschirmgröße und eingestellter Schriftgröße kann die Seitenzahl auf Ihrem Lesegerät variieren.
eBook Informationen
- Dateiformat: PDF
- Mit Kopierschutz
Sprache:
Englisch
Kopierschutz
Dieses eBook können Sie uneingeschränkt auf allen Geräten der tolino Familie lesen. Zum Lesen auf sonstigen eReadern und am PC benötigen Sie eine Adobe ID.
Kommentar zu "Neural Information Processing series: Practical Applications of Sparse Modeling"
0 Gebrauchte Artikel zu „Neural Information Processing series: Practical Applications of Sparse Modeling“
Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
---|
Schreiben Sie einen Kommentar zu "Neural Information Processing series: Practical Applications of Sparse Modeling".
Kommentar verfassen