Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Johan A. K. Suyken"'
Autor:
Zahra Karevan, Johan A. K. Suykens
Publikováno v:
Entropy, Vol 20, Iss 4, p 264 (2018)
Entropy measures have been a major interest of researchers to measure the information content of a dynamical system. One of the well-known methodologies is sample entropy, which is a model-free approach and can be deployed to measure the information
Externí odkaz:
https://doaj.org/article/f0564025f5904a1ca037575369d164de
Publikováno v:
Entropy, Vol 20, Iss 3, p 171 (2018)
This paper studies the matrix completion problems when the entries are contaminated by non-Gaussian noise or outliers. The proposed approach employs a nonconvex loss function induced by the maximum correntropy criterion. With the help of this loss fu
Externí odkaz:
https://doaj.org/article/319acbb73e9f4ea7abe1b437b18259a7
Publikováno v:
Entropy, Vol 18, Iss 5, p 182 (2016)
Spectral clustering methods allow datasets to be partitioned into clusters by mapping the input datapoints into the space spanned by the eigenvectors of the Laplacian matrix. In this article, we make use of the incomplete Cholesky decomposition (ICD)
Externí odkaz:
https://doaj.org/article/216b0c2656974ea1aa4210c3bcb3f05c
Publikováno v:
SSCI
A new methodology for identifying Multiple Input Multiple Output (MIMO) Hammerstein Systems is presented in this paper. The method consists of two stages. In the first stage, a Least Squares Support Vector Machine (LS-SVM) is used to model the nonlin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b806de4c5a445a0ccc084e28c6161bb4
https://doi.org/10.1109/ssci.2017.8280928
https://doi.org/10.1109/ssci.2017.8280928