Výsledky vyhledávání - "TORRECILLA, JOSÉ. L."

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Publikováno v: Probab. Lett., 136, pp. 15-19. Special Issue on "The role of Statistics in the era of Big Data"

Technology is generating a huge and growing availability of observa tions of diverse nature. This big data is placing data learning as a central scientific discipline. It includes collection, storage, preprocessing, visualization and, essentially, st

Externí odkaz: http://arxiv.org/abs/1806.03971

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Feature selection in functional data classification with recursive maxima hunting

Autor: Torrecilla, José L., Suárez, Alberto

Publikováno v: Advances in Neural Information Processing Systems (pp. 4835-4843)

Dimensionality reduction is one of the key issues in the design of effective machine learning methods for automatic induction. In this work, we introduce recursive maxima hunting (RMH) for variable selection in classification problems with functional

Externí odkaz: http://arxiv.org/abs/1806.02922

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On the use of reproducing kernel Hilbert spaces in functional classification

Autor: Berrendero, José R., Cuevas, Antonio, Torrecilla, José L.

The H\'ajek-Feldman dichotomy establishes that two Gaussian measures are either mutually absolutely continuous with respect to each other (and hence there is a Radon-Nikodym density for each measure with respect to the other one) or mutually singular

Externí odkaz: http://arxiv.org/abs/1507.04398

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The mRMR variable selection method: a comparative study for functional data

Autor: Berrendero, José R., Cuevas, Antonio, Torrecilla, José L.

Publikováno v: Journal of Statistical Computation and Simulation Volume 86, Issue 5, 2016

The use of variable selection methods is particularly appealing in statistical problems with functional data. The obvious general criterion for variable selection is to choose the `most representative' or `most relevant' variables. However, it is als

Externí odkaz: http://arxiv.org/abs/1507.03496

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Variable selection in functional data classification: a maxima-hunting proposal

Autor: Berrendero, José R., Cuevas, Antonio, Torrecilla, José L.

Publikováno v: Statistica Sinica 26 (2016), 619-638

Variable selection is considered in the setting of supervised binary classification with functional data $\{X(t),\ t\in[0,1]\}$. By "variable selection" we mean any dimension-reduction method which leads to replace the whole trajectory $\{X(t),\ t\in

Externí odkaz: http://arxiv.org/abs/1309.6697

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