Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Meganck, Stijn"'
Autor:
Meganck, Stijn
A partir de données d'observation classiques, il est rarement possible d'arriver à une structure de réseau bayésien qui soit complètement causale. Le point théorique auquel nous nous intéressons est l'apprentissage des réseaux bayésiens caus
Externí odkaz:
http://tel.archives-ouvertes.fr/tel-00915256
http://tel.archives-ouvertes.fr/docs/00/91/52/56/PDF/phdStijn.pdf
http://tel.archives-ouvertes.fr/docs/00/91/52/56/PDF/phdStijn.pdf
Publikováno v:
In International Journal of Approximate Reasoning December 2012 53(9):1305-1325
Publikováno v:
In International Journal of Approximate Reasoning 2007 46(2):274-299
Autor:
Taminau Jonatan, Meganck Stijn, Lazar Cosmin, Steenhoff David, Coletta Alain, Molter Colin, Duque Robin, Schaetzen Virginie de, Weiss Solís David Y, Bersini Hugues, Nowé Ann
Publikováno v:
BMC Bioinformatics, Vol 13, Iss 1, p 335 (2012)
Abstract Background With an abundant amount of microarray gene expression data sets available through public repositories, new possibilities lie in combining multiple existing data sets. In this new context, analysis itself is no longer the problem,
Externí odkaz:
https://doaj.org/article/506321dc462b4db0a844505b4bdb8bdb
Autor:
Lazar, Cosmin, Meganck, Stijn, Taminau, Jonatan, Steenhoff, David, Coletta, Alain, Molter, Colin, Weiss-Solís, David Y., Duque, Robin, Bersini, Hugues, Nowé, Ann
Publikováno v:
Briefings in Bioinformatics. Jul2013, Vol. 14 Issue 4, p469-490. 22p.
Standard Hidden Markov Models (HMMs) approaches used for condition assessment of bearings assume that all the possible system states are fixed and known a priori and that training data from all of the associated states are available. Moreover, the tr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3848::88a99524e845182b075af9d41ac627cf
https://biblio.vub.ac.be/vubir/online-adaptive-learning-of-leftright-continuous-hmm-for-bearings-condition-assessment(c40d843a-de52-4520-a308-9edf5ace0816).html
https://biblio.vub.ac.be/vubir/online-adaptive-learning-of-leftright-continuous-hmm-for-bearings-condition-assessment(c40d843a-de52-4520-a308-9edf5ace0816).html
Autor:
Taminau, Jonatan, Hillewaere, Ruben, Meganck, Stijn, Conklin, Darrell, Manderick, Bernard, Nowe, Ann
Descriptive and predictive analyses of symbolic music data assist in understanding the properties that characterize spe- cific genres, movements and composers. Subgroup Discov- ery, a machine learning technique lying on the intersection between these
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3848::bfdf264faf7be43aa61e6a4893fdec42
https://biblio.vub.ac.be/vubir/applying-subgroup-discovery-for-the-analysis-of-string-quertet-movements(84eacfd7-a2bc-4770-9068-11668a1062a2).html
https://biblio.vub.ac.be/vubir/applying-subgroup-discovery-for-the-analysis-of-string-quertet-movements(84eacfd7-a2bc-4770-9068-11668a1062a2).html
There is a vast amount of gene expression data that has been gathered in microarray studies all over the world. Many of these studies use different experimentation plans, different platforms, different method- ologies, etc. Merging information of dif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3848::1e8f41eb9e4f2e5a5d751f532bf60dff
https://biblio.vub.ac.be/vubir/validation-of-merging-techniques-for-cancer-microarray-data-sets(987e9288-5990-40ec-b6ba-f8535b3f56f0).html
https://biblio.vub.ac.be/vubir/validation-of-merging-techniques-for-cancer-microarray-data-sets(987e9288-5990-40ec-b6ba-f8535b3f56f0).html
Autor:
Taminau, Jonatan, Hillewaere, Ruben, Meganck, Stijn, Conklin, Darrell, Nowe, Ann, Manderick, Bernard
Descriptive analysis of music corpora is important to musicologists who are interested in identifying the properties of specific genres of music. In this study we present such an analysis of a large corpus of folk tunes, all labeled by their origin.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od______3848::230d6faf7b20431d8fe925883542fe7f
https://hdl.handle.net/20.500.14017/79ca6e76-fac6-4e8e-8f79-bcc4c09b85b9
https://hdl.handle.net/20.500.14017/79ca6e76-fac6-4e8e-8f79-bcc4c09b85b9