Výsledky vyhledávání - "Geert Fannes"

Bayesian applications of belief networks and multilayer perceptrons for ovarian tumor classification with rejection

Autor: Geert Fannes, Yves Moreau, Dirk Timmerman, Bart De Moor, Péter Antal

Publikováno v: Artificial intelligence in medicine. 29(1-2)

Incorporating prior knowledge into black-box classifiers is still much of an open problem. We propose a hybrid Bayesian methodology that consists in encoding prior knowledge in the form of a (Bayesian) belief network and then using this knowledge to

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa592c0218fb3422652e694d01fc1bda
https://pubmed.ncbi.nlm.nih.gov/12957780

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Probabilistic graphical models for computational biomedicine

Autor: Péter Antal, Yves Moreau, Geert Fannes, B. De Moor

Publikováno v: Europe PubMed Central
Scopus-Elsevier

Summary Background: As genomics becomes increasingly relevant to medicine, medical informatics and bioinformatics are gradually converging into a larger field that we call computational biomedicine. Objectives: Developing a computational framework th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42ef55b73250b7ca79f11bf4347c99c5
https://pubmed.ncbi.nlm.nih.gov/12743653

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Extended Bayesian Regression Models: A Symbiotic Application of Belief Networks and Multilayer Perceptrons for the Classification of Ovarian Tumors

Autor: Joos Vandewalle, Bart De Moor, Dirk Timmerman, Geert Fannes, Yves Moreau, Péter Antal

Publikováno v: Artificial Intelligence in Medicine ISBN: 9783540422945
AIME

We describe a methodology based on a dual Belief Network-Multilayer Perceptron representation to build Bayesian classifiers. This methodology combines efficiently the prior domain knowledge and statistical data. We overview how this Bayesian methodol

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2129fb8c000597ed8c5f31d25f29fcdb
https://doi.org/10.1007/3-540-48229-6_26

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On the potential of domain literature for clustering and Bayesian network learning

Autor: Geert Fannes, Patrick Glenisson, Péter Antal

Publikováno v: Scopus-Elsevier
KDD

Thanks to its increasing availability, electronic literature can now be a major source of information when developing complex statistical models where data is scarce or contains much noise. This raises the question of how to integrate information fro

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::124f3b740cd495df0b957f136744e8cf
http://www.scopus.com/inward/record.url?eid=2-s2.0-0042700675&partnerID=MN8TOARS

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