Zobrazeno 1 - 10
of 60
pro vyhledávání: '"Stéphane, Lallich"'
Publikováno v:
Bulletins et Mémoires de la Société d’Anthropologie de Paris, Vol 18, Iss 2, Pp 87-102 (2006)
Using the parish and civil records of five townships of the Valserine Valley, we have reconstructed the genealogical networks of all the individuals born in this valley from the end of the 17th century to the present day. Our goal is to make a quanti
Externí odkaz:
https://doaj.org/article/18bb08619784439b935ed73e7958c0d1
Publikováno v:
IFIP Advances in Information and Communication Technology
14th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2018)
14th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2018), May 2018, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, 519, pp.546-555, 2018, 〈http://easyconferences.eu/aiai2018/〉. 〈10.1007/978-3-319-92007-8〉
IFIP Advances in Information and Communication Technology ISBN: 9783319920061
AIAI
14th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2018)
14th International Conference on Artificial Intelligence Applications and Innovations (AIAI 2018), May 2018, Rhodes, Greece. Springer, IFIP Advances in Information and Communication Technology, 519, pp.546-555, 2018, 〈http://easyconferences.eu/aiai2018/〉. 〈10.1007/978-3-319-92007-8〉
IFIP Advances in Information and Communication Technology ISBN: 9783319920061
AIAI
International audience; Cluster analysis is widely used in the areas of machine learning and data mining. Fuzzy clustering is a particular method that considers that a data point can belong to more than one cluster. Fuzzy clustering helps obtain flex
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::829ed2d1adcb9014feccb6e50cfb6b57
http://arxiv.org/abs/1806.01552
http://arxiv.org/abs/1806.01552
Publikováno v:
IFIP Advances in Information and Communication Technology ISBN: 9783319920061
Cluster analysis is widely used in the areas of machine learning and data mining. Fuzzy clustering is a particular method that considers that a data point can belong to more than one cluster. Fuzzy clustering helps obtain flexible clusters, as needed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1a00cf107991d4ba29706aa3e4128fdc
https://doi.org/10.1007/978-3-319-92007-8
https://doi.org/10.1007/978-3-319-92007-8
Autor:
Julien Langrand-Escure, Sophie Espenel, Philippe Kilendo, Alexis Vallard, Guy de Laroche, Patrick Michaud, Michèle Ho, Gilles-Damas Froissart, Manuel Dutilleux, Majed Ben Mrad, Fabienne Chauvin, Jacques Bagur, Nicolas Magné, Stéphane Lallich, Sarah Jouan, Marie Bourdis, Catherine Massoubre, Benoîte Méry
Publikováno v:
Bulletin du Cancer. 102:845-853
Resume Introduction Le but de notre etude est d’evaluer la detresse psychologique (ou desarroi), des patientes en cours de radiotherapie pour un cancer du sein afin de depister celles necessitant une prise en charge psychologique. Methodologie L’
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6c8dde4cdb4d2c7f32d2f01f97feecf9
https://doi.org/10.1016/j.bulcan.2015.07.004
https://doi.org/10.1016/j.bulcan.2015.07.004
Publikováno v:
Neurocomputing. 160:3-17
This paper focuses on the detection of likely mislabeled instances in a learning dataset. In order to detect potentially mislabeled samples, two solutions are considered which are both based on the same framework of topological graphs. The first is a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f5d5bdb3f5abf56c8cf414eb0f813546
https://doi.org/10.1016/j.neucom.2014.10.087
https://doi.org/10.1016/j.neucom.2014.10.087
Autor:
Stéphane Lallich, Philippe Lenca
Publikováno v:
Journal of Intelligent Information Systems. 45:295-297
There are many data mining algorithms and methodologies for various fields and various problematic. Each data mining researcher/practitioner should describe the intrinsic quality of the discovered models and patterns. In addition he/she is faced with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f308251493714fc59d39c76832dc4e1f
https://doi.org/10.1007/s10844-015-0370-7
https://doi.org/10.1007/s10844-015-0370-7
Publikováno v:
Data Mining and Knowledge Discovery
Data Mining and Knowledge Discovery, Springer, 2016, 30 (5), pp.1324-1349. ⟨10.1007/s10618-015-0445-7⟩
Data Mining and Knowledge Discovery, Springer, 2016, 30 (5), pp.1324-1349. ⟨10.1007/s10618-015-0445-7⟩
International audience; We propose ClusPath, a novel algorithm for detecting general evolution tendencies in a population of entities. We show how abstract notions, such as the Swedish socio-economical model (in a political dataset) or the companies
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c943ebae4069290dda955db88b26868d
https://hdl.handle.net/10453/140056
https://hdl.handle.net/10453/140056
Publikováno v:
Computational Intelligence. 28:475-504
Many studies have shown the limits of the support/confidence framework used in Apriori -like algorithms to mine association rules. There are a lot of efficient implementations based on the antimonotony property of the support, but candidate set gener
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b21c90d8a17adc7d6dfcd3990897d017
https://doi.org/10.1111/j.1467-8640.2012.00422.x
https://doi.org/10.1111/j.1467-8640.2012.00422.x
Attribute-based format is the main data representation format used by machine learning algorithms. When the attributes do not properly describe the initial data, performance starts to degrade. Some algorithms address this problem by internally changi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e364826f8d5941dcfd1dd5381d8a583
http://arxiv.org/abs/1512.05467
http://arxiv.org/abs/1512.05467
Publikováno v:
European Journal of Operational Research
European Journal of Operational Research, Elsevier, 2008, 184 (2), pp.610-626. ⟨10.1016/j.ejor.2006.10.059⟩
European Journal of Operational Research, Elsevier, 2008, 184 (2), pp.610-626. ⟨10.1016/j.ejor.2006.10.059⟩
International audience; Data mining algorithms, especially those used for unsupervised learning, generate a large quantity of rules. In particular this applies to the APRIORI family of algorithms for the determination of association rules. It is henc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fb51adbc9ade60ad52c7cc83dd98e206
https://doi.org/10.1016/j.ejor.2006.10.059
https://doi.org/10.1016/j.ejor.2006.10.059