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Tian, Siva:

DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA - Livres de poche

2010, ISBN: 9783639288681

[ED: Taschenbuch / Paperback], [PU: VDM Verlag Dr. Müller], High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because th… Plus…

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Siva Tian:

DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA - Livres de poche

ISBN: 9783639288681

[ED: Taschenbuch], [PU: VDM Verlag Dr. Müller], Neuware - High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the … Plus…

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DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA - Siva Tian
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Siva Tian:
DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA - Livres de poche

ISBN: 9783639288681

[ED: Taschenbuch], [PU: VDM Verlag Dr. Müller], Neuware - High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the … Plus…

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Tian, Siva:
DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA - Livres de poche

2010, ISBN: 9783639288681

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DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA Siva Tian Author - nouveau livre

ISBN: 9783639288681

High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventio… Plus…

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DIMENSIONALITY REDUCTION FOR CLASSIFICATION WITH HIGH-DIMENSIONAL DATA Siva Tian Author

High-dimensional data refers to data with a large number of variables. Classifying these data is a difficult problem because the enormous number of variables poses challenges to conventional classification methods and renders many classical techniques impractical. A natural solution is to add a dimensionality reduction step before a classification technique is applied. We Propose three methods to deal with this problem: a simulated annealing (SA) based method, a multivariate adaptive stochastic search (MASS) method, and a functional adaptive classification (FAC) method. The third method considers functional predictors. They all utilize stochastic search algorithms to select a handful of optimal transformation directions from a large number of random directions in each iteration. These methods are designed to mimic variable selection type methods, such as the Lasso, or variable combination methods, such as PCA, or a method that combines the two approaches. We demonstrate the strengths of our methods on an extensive range of simulation and real-world studies.

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EAN (ISBN-13): 9783639288681
ISBN (ISBN-10): 3639288688
Livre de poche
Date de parution: 2010
Editeur: VDM Verlag Core >1 >T

Livre dans la base de données depuis 2009-12-07T03:44:47+01:00 (Paris)
Page de détail modifiée en dernier sur 2024-03-01T08:38:31+01:00 (Paris)
ISBN/EAN: 9783639288681

ISBN - Autres types d'écriture:
3-639-28868-8, 978-3-639-28868-1
Autres types d'écriture et termes associés:
Auteur du livre: tian, siva


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