ºÝºÝߣshows by User: PhilippeLeray / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: PhilippeLeray / Thu, 07 Sep 2023 13:11:56 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: PhilippeLeray ºÝºÝߣsTRAIL.pdf https://fr.slideshare.net/slideshow/slidestrailpdf/260661120 slidestrail-230907131157-4e9df0ab
Apprentissage et utilisation de mod¨¨les graphiques probabilistes pour la mod¨¦lisation de syst¨¨mes complexes, 7 sept 2023, conf¨¦rence "Recherche en IA pour la Sant¨¦ et l'Industrie", Nantes Universit¨¦ et TRAIL]]>
Apprentissage et utilisation de mod¨¨les graphiques probabilistes pour la mod¨¦lisation de syst¨¨mes complexes, 7 sept 2023, conf¨¦rence "Recherche en IA pour la Sant¨¦ et l'Industrie", Nantes Universit¨¦ et TRAIL]]> Thu, 07 Sep 2023 13:11:56 GMT https://fr.slideshare.net/slideshow/slidestrailpdf/260661120 PhilippeLeray@slideshare.net(PhilippeLeray) ºÝºÝߣsTRAIL.pdf PhilippeLeray Apprentissage et utilisation de mod¨¨les graphiques probabilistes pour la mod¨¦lisation de syst¨¨mes complexes, 7 sept 2023, conf¨¦rence "Recherche en IA pour la Sant¨¦ et l'Industrie", Nantes Universit¨¦ et TRAIL <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slidestrail-230907131157-4e9df0ab-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Apprentissage et utilisation de mod¨¨les graphiques probabilistes pour la mod¨¦lisation de syst¨¨mes complexes, 7 sept 2023, conf¨¦rence &quot;Recherche en IA pour la Sant¨¦ et l&#39;Industrie&quot;, Nantes Universit¨¦ et TRAIL
from University of Nantes
]]> 58 0 https://cdn.slidesharecdn.com/ss_thumbnails/slidestrail-230907131157-4e9df0ab-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 ºÝºÝߣsJS-PhLeray.pdf https://fr.slideshare.net/slideshow/slidesjsphleraypdf/258258201 slidesjs-phleray-230605185659-62af0305
R¨¦seaux bay¨¦siens, vers des mod¨¨les d¡¯IA directement compr¨¦hensibles et soutenables pour la mod¨¦lisation de syst¨¨mes complexes, 5 juin. 2023, colloque "Mod¨¦lisation et IA en Sciences et Technologies", Journ¨¦es Scientifiques de l'Universit¨¦ de Nantes]]>
R¨¦seaux bay¨¦siens, vers des mod¨¨les d¡¯IA directement compr¨¦hensibles et soutenables pour la mod¨¦lisation de syst¨¨mes complexes, 5 juin. 2023, colloque "Mod¨¦lisation et IA en Sciences et Technologies", Journ¨¦es Scientifiques de l'Universit¨¦ de Nantes]]> Mon, 05 Jun 2023 18:56:59 GMT https://fr.slideshare.net/slideshow/slidesjsphleraypdf/258258201 PhilippeLeray@slideshare.net(PhilippeLeray) ºÝºÝߣsJS-PhLeray.pdf PhilippeLeray R¨¦seaux bay¨¦siens, vers des mod¨¨les d¡¯IA directement compr¨¦hensibles et soutenables pour la mod¨¦lisation de syst¨¨mes complexes, 5 juin. 2023, colloque "Mod¨¦lisation et IA en Sciences et Technologies", Journ¨¦es Scientifiques de l'Universit¨¦ de Nantes <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slidesjs-phleray-230605185659-62af0305-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> R¨¦seaux bay¨¦siens, vers des mod¨¨les d¡¯IA directement compr¨¦hensibles et soutenables pour la mod¨¦lisation de syst¨¨mes complexes, 5 juin. 2023, colloque &quot;Mod¨¦lisation et IA en Sciences et Technologies&quot;, Journ¨¦es Scientifiques de l&#39;Universit¨¦ de Nantes
from University of Nantes
]]> 59 0 https://cdn.slidesharecdn.com/ss_thumbnails/slidesjs-phleray-230605185659-62af0305-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Une introduction aux R¨¦seaux bay¨¦siens https://fr.slideshare.net/slideshow/une-introduction-aux-rseaux-baysiens/240137308 pnfhlqauqteo33bvnhzt-signature-3ac208d240af7473befc671c9e28de51f162ec4983ce55def0566b3d7574ad45-poli-201215130631
Salon de la Data, 15 d¨¦cembre 2020]]>
Salon de la Data, 15 d¨¦cembre 2020]]> Tue, 15 Dec 2020 13:06:30 GMT https://fr.slideshare.net/slideshow/une-introduction-aux-rseaux-baysiens/240137308 PhilippeLeray@slideshare.net(PhilippeLeray) Une introduction aux R¨¦seaux bay¨¦siens PhilippeLeray Salon de la Data, 15 d¨¦cembre 2020 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pnfhlqauqteo33bvnhzt-signature-3ac208d240af7473befc671c9e28de51f162ec4983ce55def0566b3d7574ad45-poli-201215130631-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Salon de la Data, 15 d¨¦cembre 2020
from University of Nantes
]]> 166 0 https://cdn.slidesharecdn.com/ss_thumbnails/pnfhlqauqteo33bvnhzt-signature-3ac208d240af7473befc671c9e28de51f162ec4983ce55def0566b3d7574ad45-poli-201215130631-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Les Challenges de l'Intelligence Artificielle https://fr.slideshare.net/slideshow/les-challenges-de-lintelligence-artificielle/197743935 slidescominlabs-191126043757
Journ¨¦e CominLabs Data, AI & Robotics, 14 juin 2019]]>
Journ¨¦e CominLabs Data, AI & Robotics, 14 juin 2019]]> Tue, 26 Nov 2019 04:37:57 GMT https://fr.slideshare.net/slideshow/les-challenges-de-lintelligence-artificielle/197743935 PhilippeLeray@slideshare.net(PhilippeLeray) Les Challenges de l'Intelligence Artificielle PhilippeLeray Journ¨¦e CominLabs Data, AI & Robotics, 14 juin 2019 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slidescominlabs-191126043757-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Journ¨¦e CominLabs Data, AI &amp; Robotics, 14 juin 2019
from University of Nantes
]]> 388 0 https://cdn.slidesharecdn.com/ss_thumbnails/slidescominlabs-191126043757-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Advances in Bayesian network learning /slideshow/advances-in-bayesian-network-learning-197732561/197732561 slidesunisa-191126040708
Data Analytics Seminar, 26 nov. 2019, Univ. of South Australia, Adelaide]]>
Data Analytics Seminar, 26 nov. 2019, Univ. of South Australia, Adelaide]]> Tue, 26 Nov 2019 04:07:08 GMT /slideshow/advances-in-bayesian-network-learning-197732561/197732561 PhilippeLeray@slideshare.net(PhilippeLeray) Advances in Bayesian network learning PhilippeLeray Data Analytics Seminar, 26 nov. 2019, Univ. of South Australia, Adelaide <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slidesunisa-191126040708-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Data Analytics Seminar, 26 nov. 2019, Univ. of South Australia, Adelaide
Advances in Bayesian network learning from University of Nantes
]]> 418 2 https://cdn.slidesharecdn.com/ss_thumbnails/slidesunisa-191126040708-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Learning Probabilistic Relational Models /slideshow/learning-probabilistic-relational-models/74537113 slides-170406105124
Learning graphical models in high dimensional settings Apr 04, 2017 - Apr 07, 2017 ICMS, 15 South College Street Probabilistic Relational Models (PRMs) extend Bayesian networks to work with relational databases rather than propositional data. Existing approaches for PRM structure learning are inspired from classical methods of learning the BN structure. We will present in this talk our works about: - hybrid structure learning methods, with our adaptation of the Max-Min Hill Climbing (MMHC) algorithm proposed for Bayesian networks. - exact and anytime structure learning methods, with a preliminary work. ]]>
Learning graphical models in high dimensional settings Apr 04, 2017 - Apr 07, 2017 ICMS, 15 South College Street Probabilistic Relational Models (PRMs) extend Bayesian networks to work with relational databases rather than propositional data. Existing approaches for PRM structure learning are inspired from classical methods of learning the BN structure. We will present in this talk our works about: - hybrid structure learning methods, with our adaptation of the Max-Min Hill Climbing (MMHC) algorithm proposed for Bayesian networks. - exact and anytime structure learning methods, with a preliminary work. ]]> Thu, 06 Apr 2017 10:51:24 GMT /slideshow/learning-probabilistic-relational-models/74537113 PhilippeLeray@slideshare.net(PhilippeLeray) Learning Probabilistic Relational Models PhilippeLeray Learning graphical models in high dimensional settings Apr 04, 2017 - Apr 07, 2017 ICMS, 15 South College Street Probabilistic Relational Models (PRMs) extend Bayesian networks to work with relational databases rather than propositional data. Existing approaches for PRM structure learning are inspired from classical methods of learning the BN structure. We will present in this talk our works about: - hybrid structure learning methods, with our adaptation of the Max-Min Hill Climbing (MMHC) algorithm proposed for Bayesian networks. - exact and anytime structure learning methods, with a preliminary work. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slides-170406105124-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Learning graphical models in high dimensional settings Apr 04, 2017 - Apr 07, 2017 ICMS, 15 South College Street Probabilistic Relational Models (PRMs) extend Bayesian networks to work with relational databases rather than propositional data. Existing approaches for PRM structure learning are inspired from classical methods of learning the BN structure. We will present in this talk our works about: - hybrid structure learning methods, with our adaptation of the Max-Min Hill Climbing (MMHC) algorithm proposed for Bayesian networks. - exact and anytime structure learning methods, with a preliminary work.
Learning Probabilistic Relational Models from University of Nantes
]]> 557 4 https://cdn.slidesharecdn.com/ss_thumbnails/slides-170406105124-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 An exact approach to learning Probabilistic Relational Model /slideshow/an-exact-approach-to-learning-probabilistic-relational-model/65771335 pgm2016-160907090434
presentation given in the International Conference on Probabilistic Graphical Models, Lugano (Switzerland), Sept. 7, 2016]]>
presentation given in the International Conference on Probabilistic Graphical Models, Lugano (Switzerland), Sept. 7, 2016]]> Wed, 07 Sep 2016 09:04:34 GMT /slideshow/an-exact-approach-to-learning-probabilistic-relational-model/65771335 PhilippeLeray@slideshare.net(PhilippeLeray) An exact approach to learning Probabilistic Relational Model PhilippeLeray presentation given in the International Conference on Probabilistic Graphical Models, Lugano (Switzerland), Sept. 7, 2016 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pgm2016-160907090434-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> presentation given in the International Conference on Probabilistic Graphical Models, Lugano (Switzerland), Sept. 7, 2016
An exact approach to learning Probabilistic Relational Model from University of Nantes
]]> 276 6 https://cdn.slidesharecdn.com/ss_thumbnails/pgm2016-160907090434-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Advances in Learning with Bayesian Networks - july 2015 /slideshow/slides-meetup/50587616 slidesmeetup-150716091005-lva1-app6892
Bayesian networks (BNs) are a powerful tool for graphical representation of the underlying knowledge in the data and reasoning with incomplete or imprecise observations. BNs have been extended (or generalized) in several ways, as for instance, causal BNs, dynamic BNs, Relational BNs, ... In this lecture, we will focus on Bayesian network learning. BN learning can differ with respect to the task : generative model versus discriminative one ? Then, the learning task can also differ w.r.t the nature of the data : complete data, incomplete data, non i.i.d data, number of variables >> number of samples, data stream, presence of prior knowledge ... Given the diversity of these problems, many approaches have emerged in the literature. I will present a brief panorama of those algorithms and describe our current works in this field, with works about BN structure learning, dynamic BN structure learning and relational BN structure learning.]]>
Bayesian networks (BNs) are a powerful tool for graphical representation of the underlying knowledge in the data and reasoning with incomplete or imprecise observations. BNs have been extended (or generalized) in several ways, as for instance, causal BNs, dynamic BNs, Relational BNs, ... In this lecture, we will focus on Bayesian network learning. BN learning can differ with respect to the task : generative model versus discriminative one ? Then, the learning task can also differ w.r.t the nature of the data : complete data, incomplete data, non i.i.d data, number of variables >> number of samples, data stream, presence of prior knowledge ... Given the diversity of these problems, many approaches have emerged in the literature. I will present a brief panorama of those algorithms and describe our current works in this field, with works about BN structure learning, dynamic BN structure learning and relational BN structure learning.]]> Thu, 16 Jul 2015 09:10:05 GMT /slideshow/slides-meetup/50587616 PhilippeLeray@slideshare.net(PhilippeLeray) Advances in Learning with Bayesian Networks - july 2015 PhilippeLeray Bayesian networks (BNs) are a powerful tool for graphical representation of the underlying knowledge in the data and reasoning with incomplete or imprecise observations. BNs have been extended (or generalized) in several ways, as for instance, causal BNs, dynamic BNs, Relational BNs, ... In this lecture, we will focus on Bayesian network learning. BN learning can differ with respect to the task : generative model versus discriminative one ? Then, the learning task can also differ w.r.t the nature of the data : complete data, incomplete data, non i.i.d data, number of variables >> number of samples, data stream, presence of prior knowledge ... Given the diversity of these problems, many approaches have emerged in the literature. I will present a brief panorama of those algorithms and describe our current works in this field, with works about BN structure learning, dynamic BN structure learning and relational BN structure learning. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slidesmeetup-150716091005-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Bayesian networks (BNs) are a powerful tool for graphical representation of the underlying knowledge in the data and reasoning with incomplete or imprecise observations. BNs have been extended (or generalized) in several ways, as for instance, causal BNs, dynamic BNs, Relational BNs, ... In this lecture, we will focus on Bayesian network learning. BN learning can differ with respect to the task : generative model versus discriminative one ? Then, the learning task can also differ w.r.t the nature of the data : complete data, incomplete data, non i.i.d data, number of variables &gt;&gt; number of samples, data stream, presence of prior knowledge ... Given the diversity of these problems, many approaches have emerged in the literature. I will present a brief panorama of those algorithms and describe our current works in this field, with works about BN structure learning, dynamic BN structure learning and relational BN structure learning.
Advances in Learning with Bayesian Networks - july 2015 from University of Nantes
]]> 1065 6 https://cdn.slidesharecdn.com/ss_thumbnails/slidesmeetup-150716091005-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Learning possibilistic networks from data: a survey /slideshow/learning-possibilistic-networks-from-data-a-survey/39233677 prsentation-140918044054-phpapp02
Talk during JFRB 2014, june 2014, IHP, Paris]]>
Talk during JFRB 2014, june 2014, IHP, Paris]]> Thu, 18 Sep 2014 04:40:54 GMT /slideshow/learning-possibilistic-networks-from-data-a-survey/39233677 PhilippeLeray@slideshare.net(PhilippeLeray) Learning possibilistic networks from data: a survey PhilippeLeray Talk during JFRB 2014, june 2014, IHP, Paris <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/prsentation-140918044054-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Talk during JFRB 2014, june 2014, IHP, Paris
Learning possibilistic networks from data: a survey from University of Nantes
]]> 554 1 https://cdn.slidesharecdn.com/ss_thumbnails/prsentation-140918044054-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Evaluation des algorithmes d¡¯apprentissage de structure pour les r¨¦seaux Bay¨¦siens dynamiques https://fr.slideshare.net/slideshow/slides-jfrb2014/39233550 slidesjfrb2014-140918043747-phpapp01
Pr¨¦sentation lors des JFRB 2014, IHP, Paris, 25-27 juin 2014.]]>
Pr¨¦sentation lors des JFRB 2014, IHP, Paris, 25-27 juin 2014.]]> Thu, 18 Sep 2014 04:37:46 GMT https://fr.slideshare.net/slideshow/slides-jfrb2014/39233550 PhilippeLeray@slideshare.net(PhilippeLeray) Evaluation des algorithmes d¡¯apprentissage de structure pour les r¨¦seaux Bay¨¦siens dynamiques PhilippeLeray Pr¨¦sentation lors des JFRB 2014, IHP, Paris, 25-27 juin 2014. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slidesjfrb2014-140918043747-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Pr¨¦sentation lors des JFRB 2014, IHP, Paris, 25-27 juin 2014.
from University of Nantes
]]> 845 4 https://cdn.slidesharecdn.com/ss_thumbnails/slidesjfrb2014-140918043747-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Random Generation of Relational Bayesian Networks /slideshow/jfrb/39233222 jfrb-140918042957-phpapp01
Only the title page (g¨¦n¨¦ration al¨¦atoire de r¨¦seaux Bay¨¦siens relationnels) is in French Presentation during JFRB¡¯14 25-27 juin, IHP, Paris, France ]]>
Only the title page (g¨¦n¨¦ration al¨¦atoire de r¨¦seaux Bay¨¦siens relationnels) is in French Presentation during JFRB¡¯14 25-27 juin, IHP, Paris, France ]]> Thu, 18 Sep 2014 04:29:57 GMT /slideshow/jfrb/39233222 PhilippeLeray@slideshare.net(PhilippeLeray) Random Generation of Relational Bayesian Networks PhilippeLeray Only the title page (g¨¦n¨¦ration al¨¦atoire de r¨¦seaux Bay¨¦siens relationnels) is in French Presentation during JFRB¡¯14 25-27 juin, IHP, Paris, France <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jfrb-140918042957-phpapp01-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Only the title page (g¨¦n¨¦ration al¨¦atoire de r¨¦seaux Bay¨¦siens relationnels) is in French Presentation during JFRB¡¯14 25-27 juin, IHP, Paris, France
Random Generation of Relational Bayesian Networks from University of Nantes
]]> 498 2 https://cdn.slidesharecdn.com/ss_thumbnails/jfrb-140918042957-phpapp01-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Introduction aux mod¨¨les graphiques probabilistes https://fr.slideshare.net/slideshow/slides-ph-leray/32498717 slidesphleray-140319120141-phpapp02
Journ¨¦e GRCE Mod¨¨les Graphiques, 20 Juin 2011, Paris, France]]>
Journ¨¦e GRCE Mod¨¨les Graphiques, 20 Juin 2011, Paris, France]]> Wed, 19 Mar 2014 12:01:41 GMT https://fr.slideshare.net/slideshow/slides-ph-leray/32498717 PhilippeLeray@slideshare.net(PhilippeLeray) Introduction aux mod¨¨les graphiques probabilistes PhilippeLeray Journ¨¦e GRCE Mod¨¨les Graphiques, 20 Juin 2011, Paris, France <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slidesphleray-140319120141-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Journ¨¦e GRCE Mod¨¨les Graphiques, 20 Juin 2011, Paris, France
from University of Nantes
]]> 1690 43 https://cdn.slidesharecdn.com/ss_thumbnails/slidesphleray-140319120141-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Ontological knowledge integration for Bayesian network structure learning /slideshow/intgration-de-connaissances-ontologiques-pour-lapprentissage-des-rseaux-bayesiens/32497956 slidesaafd-140319114449-phpapp02
Int¨¦gration de connaissances ontologiques pour l'apprentissage des r¨¦seaux bay¨¦siens 5¨¨mes Journ¨¦es th¨¦matiques "Apprentissage Artificiel & Fouille de Donn¨¦es", 28 juin 2012, Universit¨¦ Paris 13, France. (title in French, but slides english) ]]>
Int¨¦gration de connaissances ontologiques pour l'apprentissage des r¨¦seaux bay¨¦siens 5¨¨mes Journ¨¦es th¨¦matiques "Apprentissage Artificiel & Fouille de Donn¨¦es", 28 juin 2012, Universit¨¦ Paris 13, France. (title in French, but slides english) ]]> Wed, 19 Mar 2014 11:44:49 GMT /slideshow/intgration-de-connaissances-ontologiques-pour-lapprentissage-des-rseaux-bayesiens/32497956 PhilippeLeray@slideshare.net(PhilippeLeray) Ontological knowledge integration for Bayesian network structure learning PhilippeLeray Int?¨¦gration de connaissances ontologiques pour l'apprentissage des r?¨¦seaux bay?¨¦siens 5¨¨mes Journ¨¦es th¨¦matiques "Apprentissage Artificiel & Fouille de Donn¨¦es", 28 juin 2012, Universit¨¦ Paris 13, France. (title in French, but slides english) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slidesaafd-140319114449-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Int?¨¦gration de connaissances ontologiques pour l&#39;apprentissage des r?¨¦seaux bay?¨¦siens 5¨¨mes Journ¨¦es th¨¦matiques &quot;Apprentissage Artificiel &amp; Fouille de Donn¨¦es&quot;, 28 juin 2012, Universit¨¦ Paris 13, France. (title in French, but slides english)
Ontological knowledge integration for Bayesian network structure learning from University of Nantes
]]> 1516 3 https://cdn.slidesharecdn.com/ss_thumbnails/slidesaafd-140319114449-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Des mod?¨¨les graphiques probabilistes aux mod?eles graphiques de dur?¨¦e https://fr.slideshare.net/slideshow/slides-pfa/32497487 slidespfa-140319113341-phpapp02
journ¨¦e Automates Probabilistes LINA, 8 nov. 2013, Nantes, France - introduction aux mod¨¨les graphiques probabilistes (MGP) - et aux MGP dynamiques - un exemple original : les mod¨¨les graphiques de dur¨¦e]]>
journ¨¦e Automates Probabilistes LINA, 8 nov. 2013, Nantes, France - introduction aux mod¨¨les graphiques probabilistes (MGP) - et aux MGP dynamiques - un exemple original : les mod¨¨les graphiques de dur¨¦e]]> Wed, 19 Mar 2014 11:33:40 GMT https://fr.slideshare.net/slideshow/slides-pfa/32497487 PhilippeLeray@slideshare.net(PhilippeLeray) Des mod?¨¨les graphiques probabilistes aux mod?eles graphiques de dur?¨¦e PhilippeLeray journ¨¦e Automates Probabilistes LINA, 8 nov. 2013, Nantes, France - introduction aux mod¨¨les graphiques probabilistes (MGP) - et aux MGP dynamiques - un exemple original : les mod?¨¨les graphiques de dur?¨¦e <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/slidespfa-140319113341-phpapp02-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> journ¨¦e Automates Probabilistes LINA, 8 nov. 2013, Nantes, France - introduction aux mod¨¨les graphiques probabilistes (MGP) - et aux MGP dynamiques - un exemple original : les mod?¨¨les graphiques de dur?¨¦e
from University of Nantes
]]> 1128 24 https://cdn.slidesharecdn.com/ss_thumbnails/slidespfa-140319113341-phpapp02-thumbnail.jpg?width=120&height=120&fit=bounds presentation 000000 http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-PhilippeLeray-48x48.jpg?cb=1694092228 https://sites.google.com/site/codpleray/ https://cdn.slidesharecdn.com/ss_thumbnails/slidestrail-230907131157-4e9df0ab-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/slidestrailpdf/260661120 ºÝºÝߣsTRAIL.pdf https://cdn.slidesharecdn.com/ss_thumbnails/slidesjs-phleray-230605185659-62af0305-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/slidesjsphleraypdf/258258201 ºÝºÝߣsJS-PhLeray.pdf https://cdn.slidesharecdn.com/ss_thumbnails/pnfhlqauqteo33bvnhzt-signature-3ac208d240af7473befc671c9e28de51f162ec4983ce55def0566b3d7574ad45-poli-201215130631-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/une-introduction-aux-rseaux-baysiens/240137308 Une introduction aux R...