Zobrazeno 1 - 10
of 3 536
pro vyhledávání: '"Kernel embedding of distributions"'
Publikováno v:
Journal of Data Science. 13:323-340
Probabilistic topic models have become a standard in modern machine learning to deal with a wide range of applications. Representing data by dimensional reduction of mixture proportion extracted from topic models is not only richer in semantics inter
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::53dfeab01ec8eb1781030d4c94ef5850
https://doi.org/10.6339/jds.201504_13(2).0006
https://doi.org/10.6339/jds.201504_13(2).0006
Autor:
Benoit Quenneville, Silvia Bianconcini
Publikováno v:
Studies of Applied Economics. 28:553-574
Recently, reproducing kernel Hilbert spaces have been introduced to provide a common approach for studying several nonparametric estimators used for smoothing functional time series data (Dagum and Bianconcini, 2006 and 2008). The reproducing kernel
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2d7becde04991e12933b236800d96c88
https://doi.org/10.25115/eea.v28i3.4741
https://doi.org/10.25115/eea.v28i3.4741
Autor:
Gauthier, Bertrand
We study the overall framework surrounding the Nyström approximation of integral operators with positive-semidefinite (PSD) kernels and the Nyström method for PSD-matrix approximation. These two methods correspond to two distinct approximation sche
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https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::f4a0f0f7901823f1cc0350ab8a5b891d
https://hal.archives-ouvertes.fr/hal-03207443v4/document
https://hal.archives-ouvertes.fr/hal-03207443v4/document
Akademický článek
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Publikováno v:
AAAI
Heterogeneous Transfer Learning (HTL) aims to solve transfer learning problems where a source domain and a target domain are of heterogeneous types of features. Most existing HTL approaches either explicitly learn feature mappings between the heterog
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bd80f8e9264d6a92533ab492c520cb45
https://doi.org/10.1609/aaai.v33i01.33018602
https://doi.org/10.1609/aaai.v33i01.33018602
Autor:
Rui Wang, Yuesheng Xu
Publikováno v:
Applied and Computational Harmonic Analysis. 46:569-623
Motivated by the need of processing non-point-evaluation functional data, we introduce the notion of functional reproducing kernel Hilbert spaces (FRKHSs). This space admits a unique functional reproducing kernel which reproduces a family of continuo
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https://explore.openaire.eu/search/publication?articleId=doi_________::f2b4e893b991ff1aba50893bcd2caa7c
https://doi.org/10.1016/j.acha.2017.07.003
https://doi.org/10.1016/j.acha.2017.07.003
Publikováno v:
IEEE Transactions on Systems, Man, and Cybernetics: Systems. 49:766-775
For most sparse coding methods, data samples are first encoded as hand-crafted features, followed by another separate learning step that generates dictionary and sparse codes. However, such feature representations may not be optimally compatible with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e2ce9b70f224fd93146650751ef4bbde
https://doi.org/10.1109/tsmc.2017.2736248
https://doi.org/10.1109/tsmc.2017.2736248
Publikováno v:
Pattern Recognition Letters. 119:86-93
Deblurring is to restore a latent clear image as well as to estimate an underlying blur kernel from a single blurry image. Motion blur kernel size is a significant input parameter of existing deblurring algorithms. Setting the size manually, which is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2efb8dc984a16e0d99d1510cdb75356d
https://doi.org/10.1016/j.patrec.2017.08.017
https://doi.org/10.1016/j.patrec.2017.08.017
Publikováno v:
Computational Statistics. 34:1889-1909
In the context of computer experiments, metamodels are largely used to represent the output of computer codes. Among these models, Gaussian process regression (kriging) is very efficient see e.g Snelson (Flexible and efficient Gaussian process models
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4537fac3212ac847890b26517df46496
https://doi.org/10.1007/s00180-019-00874-2
https://doi.org/10.1007/s00180-019-00874-2
Autor:
Mohammed Al-Smadi
Publikováno v:
Ain Shams Engineering Journal, Vol 9, Iss 4, Pp 2517-2525 (2018)
In this article, we introduce a novel numerical scheme, the iterative reproducing kernel method (IRKM), for providing numerical approximate solutions of a certain class of time-fractional boundary value problem within favorable aspects of the reprodu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4bc5a230cfa6a5bf0e300a340cf5c487
https://doi.org/10.1016/j.asej.2017.04.006
https://doi.org/10.1016/j.asej.2017.04.006