Rank A conferences : DSAA ECML/PKDD ECSQARU IDA IJCNN

[Boulahmel et al., 2024]
Boulahmel, A., Djelil, F., Gilliot, J.-M., Leray, P., and Smits, G. (2024). Mining discriminative sequential patterns of self-regulated learners. In Sifaleras, A. and Lin, F., editors, Generative Intelligence and Intelligent Tutoring Systems, pages 137-149, Cham. Springer Nature Switzerland. [ DOI | http ] 

[Fathallah et al., 2023]
Fathallah, W., Ben Amor, N., and Leray, P. (2023). An optimized quantum circuit representation of bayesian networks. In Proceedings of the 17th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2023), pages 1-11, Lens, France. [ http ] 

[Ferhat et al., 2022]
Ferhat, M., Leray, P., Ritou, M., and Le Du, N. (2022). Iterative knowledge discovery for fault detection in manufacturing systems. In Knowledge-Based and Intelligent Information & Engineering Systems 26th Annual Conference, KES-2022, pages 1-10. Knowledge-Based and Intelligent Information & Engineering Systems 26th Annual Conference, KES-2022 Verona, Italy. [ DOI | http ]

[Monvoisin et al., 2021b]
Monvoisin, M., Leray, P., and Ritou, M. (2021b). Unsupervised condition monitoring with bayesian networks: an application on high speed machining. In 31th European Safety and Reliability Conference, ESREL 2021, pages 1990-1997, Angers, France. [ DOI | http ]

[Benikhlef et al., 2021]
Benikhlef, S., Leray, P., Raschia, G., M.Ben Messaoud, and Sakly, F. (2021). Multi-task transfer learning for Bayesian network structures. In Proceedings of the 16th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2021), pages 217-228, Prague, Czechia. [ DOI | http ]

[Monvoisin et al., 2021a]
Monvoisin, M., Leray, P., and Ritou, M. (2021a). Unsupervised co-training of bayesian networks for condition prediction. In 34th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2021, July 26-29, 2021, Kuala Lumpur, Malaysia, pages 577-588. [ DOI | http ]

[Dufraisse et al., 2020]
Dufraisse, E., Leray, P., Nedellec, R., and Benkhelif, T. (2020). Interactive anomaly detection in mixed tabular data using bayesian networks. In Jaeger, M. and Nielsen, T. D., editors, Proceedings of the 10th International Conference on Probabilistic Graphical Models (PGM 2020), volume 138 of Proceedings of Machine Learning Research, pages 1-12, Aalborg, Denmark. PMLR. [ DOI | http ]

[Antakly et al., 2019]
Antakly, D., Delahaye, B., and Leray, P. (2019). Graphical event model learning and verification for security assessment. In 32th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, July 9-11, 2019, Graz, Austria, pages 245-252. Springer International Publishing. [ DOI | http ]

[Kante and Leray, 2019]
Kante, T. and Leray, P. (2019). A probabilistic relational model approach for fault tree modeling with spatial information and resource management. In 32th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2019, July 9-11, 2019, Graz, Austria, pages 555-563. Springer International Publishing. [ DOI | http ]

[Monvoisin and Leray, 2019]
Monvoisin, M. and Leray, P. (2019). Multi-task transfer learning for timescale graphical event models. In Proceedings of the 15th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2019), pages 313-323, Belgrade, Serbia. [ DOI | http ]

[Rincé et al., 2018]
Rincé, R., Kervarc, R., and Leray, P. (2018). Complex event processing under uncertainty using markov chains, constraints, and sampling. In Benzmüller, C., Ricca, F., Parent, X., and Roman, D., editors, Rules and Reasoning, pages 147-163, Cham. Springer International Publishing. [ DOI | http ]

[Chulyadyo and Leray, 2018]
Chulyadyo, R. and Leray, P. (2018). Using probabilistic relational models to generate synthetic spatial or non-spatial databases. In Proceedings of IEEE 12th International Conference on Research Challenges in Information Science (IEEE RCIS'2018), pages 1-12, Nantes, France. [ DOI | http ]

[El Abri et al., 2018]
El Abri, M., Leray, P., and Essoussi, N. (2018). Daper joint learning from partially structured graph databases. In Proceedings of the third annual International Conference on Digital Economy (ICDEc 2018), pages 129-138, Brest, France. [ DOI | http ]

[van der Gaag and Leray, 2018]
van der Gaag, L. C. and Leray, P. (2018). Qualitative probabilistic relational models. In Ciucci, D., Pasi, G., and Vantaggi, B., editors, Scalable Uncertainty Management, pages 276-289, Cham. Springer International Publishing. [ DOI | http ]

[Benhaddou and Leray, 2017]
Benhaddou, Y. and Leray, P. (2017). Customer relationship management and small data - application of bayesian network elicitation techniques for building a lead scoring model. In Proceedings of the 14th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2017), pages 251-255, Hammamet, Tunisia. [ DOI | http ]

[El Abri et al., 2017]
El Abri, M., Leray, P., and Essoussi, N. (2017). Learning probabilistic relational models with (partially structured) graph databases. In Proceedings of the 14th ACS/IEEE International Conference on Computer Systems and Applications (AICCSA 2017), pages 256-263, Hammamet, Tunisia. [ DOI | http ]

[Haddad et al., 2017a]
Haddad, M., Leray, P., and Ben Amor, N. (2017a). Possibilistic MDL: a new possibilistic likelihood based score function for imprecise data. In Proceedings of the Fourteenth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2017), pages 435-445, Lugano, Switzerland. [ DOI | http ]

[Kante and Leray, 2017]
Kante, T. and Leray, P. (2017). A probabilistic relational model approach for fault tree modeling. In Benferhat, S., Tabia, K., and Ali, M., editors, Advances in Artificial Intelligence: From Theory to Practice: 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Arras, France, June 27-30, 2017, Proceedings, Part II, pages 154-162, Cham. Springer International Publishing. [ DOI | http ]

[Haddad et al., 2017b]
Haddad, M., Levray, P. L. A., and Tabia, K. (2017b). Learning the parameters of possibilistic networks from data: Empirical comparison. In Proceedings of the Thirtieth International Florida Artificial Intelligence Research Society Conference (FLAIRS 30), pages 736-741, Marco Island, USA. [ DOI | http ]

[Rincé et al., 2017]
Rincé, R., Kervarc, R., and Leray, P. (2017). On the use of walksat based algorithms for mln inference in some realistic applications. In Benferhat, S. and Tabia, Karimand Ali, M., editors, Advances in Artificial Intelligence: From Theory to Practice: 30th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2017, Arras, France, June 27-30, 2017, Proceedings, Part II, pages 121-131, Cham. Springer International Publishing. [ DOI | http ]

[Ben Ishak et al., 2016]
Ben Ishak, M., Leray, P., and Ben Amor, N. (2016). A hybrid approach for probabilistic relational models structure learning. In Proceedings of the 15th International Symposium on Intelligent Data Analysis (IDA 2016), pages 38-49, Stockholm, Sweden. [ DOI | http ]

[Ettouzi et al., 2016]
Ettouzi, N., Leray, P., and Ben Messaoud, M. (2016). An exact approach to learning probabilistic relational model. In Proceedings of the 8th International Conference on Probabilistic Graphical Models (PGM 2016), page 171–182, Lugano, Switzerland. [ http ]

[Chulyadyo and Leray, 2015]
Chulyadyo, R. and Leray, P. (2015). Integrating spatial information into probabilistic relational model. In Proceedings of 2015 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA'2015), pages 1-8, Paris, France. [ DOI | http ]

[Coutant et al., 2015a]
Coutant, A., Capitaine, H. L., and Leray, P. (2015a). On the equivalence between regularized nmf and similarity-augmented graph partitioning. In Proceedings of the 23th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2015), pages 531-536, Bruges, Belgium. [ http ]

[Coutant et al., 2015b]
Coutant, A., Capitaine, H. L., and Leray, P. (2015b). Probabilistic relational models with clustering uncertainty. In Proceedings of IEEE International Joint Conference on Neural Networks (IJCNN 2015), pages 1-8, Killarney, Ireland. [ DOI | http ]

[Haddad et al., 2015b]
Haddad, M., Leray, P., and Ben Amor, N. (2015b). Learning possibilistic networks from data: a survey. In Proceedings of the 16th World Congress of the International Fuzzy Systems Association (IFSA) and the 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), pages 1-8, Gijon, Spain. [ DOI | http ]

[Phan et al., 2015]
Phan, D.-T., Sinoquet, C., and Leray, P. (2015). Modeling genetical data with forests of latent trees for applications in association genetics at a large scale - which clustering method should be chosen? In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS 2015), pages 5-16, Lisbon, Portugal. [ DOI | http ]

[Ramstein and Leray, 2015]
Ramstein, G. and Leray, P. (2015). Cpd tree learning using contexts as background knowledge. In Proceedings of the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2015), pages ?-?, Compiègne, France. [ DOI | http ]

[Haddad et al., 2015a]
Haddad, M., Leray, P., and Ben Amor, N. (2015a). Evaluating product-based possibilistic networks learning algorithms. In Proceedings of the 13th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2015), pages 312-321, Compiegne, France. [ DOI | http ]

[Ben Ishak et al., 2014]
Ben Ishak, M., Leray, P., and Ben Amor, N. (2014). Random generation and population of probabilistic relational models and databases. In Proceedings of the 26th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2014), pages 756-763, Limassol, Cyprus. [ DOI | http ]

[Chulyadyo and Leray, 2014]
Chulyadyo, R. and Leray, P. (2014). A personalized recommender system from probabilistic relational model and users' preferences. In Knowledge-Based and Intelligent Information & Engineering Systems 18th Annual Conference, KES-2014, pages 1063 - 1072. Knowledge-Based and Intelligent Information & Engineering Systems 18th Annual Conference, KES-2014 Gdynia, Poland, September 2014 Proceedings. [ DOI | http ]

[Coutant et al., 2014]
Coutant, A., Leray, P., and Le Capitaine, H. (2014). Learning probabilistic relational models using non-negative matrix factorization. In Proceedings of the 27th International Conference of the Florida Artificial Intelligence Research Society (FLAIRS-27), pages 490-495, Pensacola Beach, USA. [ DOI | http ]

[Le Dorze et al., 2014]
Le Dorze, A., Duval, B., Garcia, L., Genest, D., Leray, P., and Loiseau, S. (2014). Probabilistic cognitive maps - semantics of a cognitive map when the values are assumed to be probabilities. In Proceedings of the 6th International Conference on Agents and Artificial Intelligence (ICAART 2014), pages 52-62, Angers, France. [ DOI | http ]

[Ben Ishak et al., 2013]
Ben Ishak, M., Ben Amor, N., and Leray, P. (2013). A relational bayesian network-based recommender system architecture. In Proceedings of the 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO 2013), pages 1-6, Hammamet, Tunisia. [ DOI | http ]

[Ben Messaoud et al., 2013]
Ben Messaoud, M., Leray, P., and Ben Amor, N. (2013). Active learning of causal bayesian networks using ontologies: a case study. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2013), pages 1-6, Dallas, USA. [ DOI | http ]

[Haddad et al., 2013]
Haddad, M., Ben Amor, N., and Leray, P. (2013). Imputation of possibilistic data for structural learning of directed acyclic graphs. In Proceedings of the Tenth International Workshop on Fuzzy Logic and Applications (WILF 2013), pages 68-76, Genova, Italy. [ DOI ]

[Trabelsi et al., 2013a]
Trabelsi, G., Leray, P., Ben Ayeb, M., and Alimi, A. (2013a). Benchmarking dynamic bayesian network structure learning algorithms. In Proceedings of the 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO 2013), pages 1-6, Hammamet, Tunisia. [ DOI | http ]

[Trabelsi et al., 2013b]
Trabelsi, G., Leray, P., Ben Ayeb, M., and Alimi, A. (2013b). Dynamic mmhc : a local search algorithm for dynamic bayesian network structure learning. In Proceedings of the Twelfth International Symposium on Intelligent Data Analysis (IDA 2013), pages 392-403, London, UK. [ DOI | http ]

[Yasin and Leray, 2013]
Yasin, A. and Leray, P. (2013). Incremental bayesian network structure learning in high dimensional domains. In Proceedings of the 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO 2013), pages 1-6, Hammamet, Tunisia. [ DOI | http ]

[Jarraya et al., 2012a]
Jarraya, A., Leray, P., and Masmoudi, A. (2012a). Discrete exponential bayesian networks structure learning for density estimation. In Proc. of the 2012 Eighth International Conference on Intelligent Computing (ICIC 2012), CCIS, pages 146-151, Huangshan, China. Springer. [ DOI | http ]

[Jarraya et al., 2012b]
Jarraya, A., Masmoudi, A., and Leray, P. (2012b). A new implicit parameter estimation for conditional gaussian bayesian networks. In 10th International FLINS Conference on Uncertainty Modeling in Knowledge Engineering and Decision Making (FLINS 2012), pages ?-?, Istanbul, Turkey. [ http ]

[Sinoquet et al., 2012]
Sinoquet, C., Mourad, R., and Leray, P. (2012). Forests of latent tree models for the detection of genetic associations. In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS 2012), pages 5-14, Vilamoura, Algarve, Portugal. [ http ]

[Ammar and Leray, 2011]
Ammar, S. and Leray, P. (2011). Mixture of markov trees for bayesian network structure learning with small datasets in high dimensional space. In Proceedings of the 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2011), pages 229-238, Belfast, Northern Ireland. [ DOI | http ]

[Ben Ishak et al., 2011a]
Ben Ishak, M., Leray, P., and Ben Amor, N. (2011a). Ontology-based generation of object oriented bayesian networks. In Nicholson, A., editor, Proceedings of the Eighth UAI Bayesian Modeling Applications Workshop (UAI-AW 2011), volume 818, pages 9-17, Barcelona, Spain. CEUR-WS.org. [ http ]

[Ben Ishak et al., 2011b]
Ben Ishak, M., Leray, P., and Ben Amor, N. (2011b). A two-way approach for probabilistic graphical models structure learning and ontology enrichment. In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD 2011) part of the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management IC3K, pages 189-194, Paris, France. [ DOI | http ]

[Ben Messaoud et al., 2011b]
Ben Messaoud, M., Leray, P., and Ben Amor, N. (2011b). Semcado: a serendipitous strategy for learning causal bayesian networks using ontologies. In Proceedings of the 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2011), pages 182-193, Belfast, Northern Ireland. [ DOI | http ]

[Ben Messaoud et al., 2011a]
Ben Messaoud, M., Leray, P., and Ben Amor, N. (2011a). Semcado: a serendipitous causal discovery algorithm for ontology evolution. In The IJCAI-11 Workshop on Automated Reasoning about Context and Ontology Evolution (ARCOE-11), pages 43-47, Barcelona, Spain. [ http ]

[Jarraya et al., 2011]
Jarraya, A., Leray, P., and Masmoudi, A. (2011). Discrete exponential bayesian networks: an extension of bayesian networks to discrete natural exponential families. In 23rd IEEE International Conference on Tools with Artificial Intelligence (ICTAI'2011), pages 205-208, Boca Raton, Florida, USA. [ DOI | http ]

[Nguyen et al., 2011a]
Nguyen, H., Leray, P., and Ramstein, G. (2011a). Summarizing and visualizing a set of bayesian networks with quasi essential graphs. In Proceedings of The 14th Conference of the ASMDA (Applied Stochastic Models and Data Analysis) International Society (ASMDA 2011), pages 1062-1069, Rome, Italy. [ http ]

[Nguyen et al., 2011b]
Nguyen, H.-T., Leray, P., and Ramstein, G. (2011b). Multiple hypothesis testing and quasi essential graph for comparing two sets of bayesian networks. In Koenig, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R., and Jain, L., editors, Knowlege-Based and Intelligent Information and Engineering Systems, volume 6882 of Lecture Notes in Computer Science, pages 176-185. Springer Berlin / Heidelberg. [ DOI | http ]

[Schnitzler et al., 2011]
Schnitzler, F., Ammar, S., Leray, P., Geurts, P., and Wehenkel, L. (2011). Efficiently approximating markov tree bagging for high-dimensional density estimation. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2011), pages 113-128, Athens, Greece. [ DOI | http ]

[Tabia et al., 2011]
Tabia, K., Benferhat, S., Leray, P., and Mé, L. (2011). Alert correlation in intrusion detection: Combining ai-based approaches for exploiting security operators’ knowledge and preferences. In The third IJCAI-11 Workshop on Intelligent Security (Security and Artificial Intelligence SECART-11), pages 42-49, Barcelona, Spain. [ DOI | http ]

[Yasin and Leray, 2011a]
Yasin, A. and Leray, P. (2011a). iMMPC: A local search approach for incremental bayesian network structure learning. In Gama, J., Bradley, E., and Hollmén, J., editors, Advances in Intelligent Data Analysis X, Proc. of the Tenth International Symposium on Intelligent Data Analysis (IDA 2011), volume 7014 of Lecture Notes in Computer Science, pages 401-412. Springer Berlin / Heidelberg. [ DOI | http ]

[Yasin and Leray, 2011b]
Yasin, A. and Leray, P. (2011b). Local skeleton discovery for incremental bayesian network structure learning. In Proc. of the 1st IEEE Int Conference on Computer Networks and Information Technology (ICCNIT), pages 309-314, Peshawar, Pakistan. [ http ]

[Ammar et al., 2010a]
Ammar, S., Leray, P., Schnitzler, F., and Wehenkel, L. (2010a). Sub-quadratic markov tree mixture learning based on randomizations of the chow-liu algorithm. In the Fifth European Workshop on Probabilistic Graphical Models (PGM-2010), pages 17-25, Helsinki, Finland. [ http ]

[Ammar et al., 2010b]
Ammar, S., Leray, P., and Wehenkel, L. (2010b). Sub-quadratic markov tree mixture models for probability density estimation. In Proceedings of the 19th International Conference on Computational Statistics (COMPSTAT 2010), pages 673-680, Paris, France. [ http ]

[Mourad et al., 2010]
Mourad, R., Sinoquet, C., and Leray, P. (2010). Learning hierarchical bayesian networks for genome-wide association studies. In Proceedings of the 19th International Conference on Computational Statistics (COMPSTAT 2010), pages 549-556, Paris, France. [ DOI | http ]

[Schnitzler et al., 2010]
Schnitzler, F., Leray, P., and Wehenkel, L. (2010). Towards sub-quadratic learning of probability density models in the form of mixtures of trees. In Proceedings of the 18th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2010), pages 219-224, Bruges, Belgium. [ http ]

[Tabia and Leray, 2010a]
Tabia, K. and Leray, P. (2010a). Bayesian network-based approaches for severe attack prediction and handling idss' reliability. In Hüllermeier, E., Kruse, R., and Hoffmann, F., editors, Information Processing and Management of Uncertainty in Knowledge-Based Systems, volume 81 of Communications in Computer and Information Science, pages 632-642. Springer. [ DOI | http ]

[Tabia and Leray, 2010b]
Tabia, K. and Leray, P. (2010b). Handling idss' reliability in alert correlation: A bayesian network-based model for handling ids's reliability and controlling prediction/false alarm rate tradeoffs. In Proceedings of the International Conference on Security and Cryptography (SECRYPT'2010), pages 1-11, Athens, Greece. [ DOI | http ]

[Ammar et al., 2009]
Ammar, S., Leray, P., Defoumy, B., and Wehenkel, L. (2009). Probability density estimation by perturbing and combining tree structured Markov networks. In Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2009), pages 156-167, Verona, Italy. [DOI | http ]

[Ben Messaoud et al., 2009]
Ben Messaoud, M., Leray, P., and Ben Amor, N. (2009). Integrating ontological knowledge for iterative causal discovery and vizualisation. In Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2009), pages 168-179, Verona, Italy. [ DOI | http ]

[Ammar et al., 2008]
Ammar, S., Leray, P., Defoumy, B., and Wehenkel, L. (2008). High-dimensional probability density estimation with randomized ensembles of tree structured bayesian networks. In Proceedings of the fourth European Workshop on Probabilistic Graphical Models (PGM'08), pages 9-16, Hirtshals, Denmark. [ .pdf ]

[Bouillaut et al., 2008]
Bouillaut, L., Francois, O., Leray, P., Aknin, P., and Dubois, S. (2008). Dynamic bayesian networks modelling maintenance strategies: Prevention of broken rails. In Proceedings of 8th World Congress on Railway Research WCCR'08, pages ??-??, Seoul, Korea.  [ http ]

[Donat et al., 2008a]
Donat, R., Bouillaut, L., Aknin, P., and Leray, P. (2008a). Reliability analysis using graphical duration models. In Third International Conference on Availability, Reliability and Security (ARES 2008), pages 795-800. [ DOI | http ]

[Donat et al., 2008b]
Donat, R., Bouillaut, L., Aknin, P., Leray, P., and Bondeux, S. (2008b). Specific graphical models for analyzing the reliability. In Proceedings of 16th Mediterranean Conference on Control and Automation MED'08, pages 621 - 626, Ajaccio, France. [ DOI | http ]

[Donat et al., 2007]
Donat, R., Bouillaut, L., Aknin, P., Leray, P., and Levy, D. (2007). A generic approach to model complex system reliability using graphical duration models. In Proceedings of Mathematical Methods in Reliability: Methodology and Practice (MMR 2007), pages ??-?? [ http ]

[Faour et al., 2007]
Faour, A., Leray, P., and Eter, B. (2007). Growing hierarchical self-organizing map for alarm filtering in network intrusion detection systems. In The 2007 International conference on New Technologes, Mobility and Security (NTMS'2007), pages ?-?, Paris, France. [ DOI | http ]

[Francois and Leray, 2007]
Francois, O. and Leray, P. (2007). Generation of incomplete test-data using bayesian networks. In Proceedings of IEEE IJCNN, International Joint Conference on Neural Networks, pages 2391-2396. [ DOI | http ]

[Meganck et al., 2007]
Meganck, S., Leray, P., and Manderick, B. (2007). Causal graphical models with latent variables: Learning and inference. In Ninth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty ECSQARU 2007, pages 5-16. [ DOI | http ]

[Faour et al., 2006b]
Faour, A., Leray, P., and Eter, B. (2006b). A SOM and bayesian network architecture for alert filtering in network intrusion detection systems. In 2nd IEEE International Conference On Information and Communication Technologies: From Theory to Applications (ICTTA 2006), pages 1161-1166, Damascus, Syria. [ DOI ]

[Faour et al., 2006a]
Faour, A., Leray, P., and Eter, B. (2006a). Automated filtering of network intrusion detection alarms. In First Joint Conference on Security in Network Architectures (SAR) and Security of Information Systems (SSI), pages 277-291, Seignosse, France.

[Francois and Leray, 2006]
Francois, O. and Leray, P. (2006). Learning the tree augmented naive bayes classifier from incomplete datasets. In The third European Workshop on Probabilistic Graphical Models PGM'06, pages 91-98, Prague, Czech Republic. [ .pdf ]

[Maes and Leray, 2006]
Maes, S. and Leray, P. (2006). Multi-agent causal models for dependability analysis. In First International Conference on Availability, Reliability and Security (ARES 2006), Bayesian Networks in Dependability (BND 2006) workshop, pages 794-798, Vienna, Austria. IEEE Computer Society. [ DOI ]

[Meganck et al., 2006a]
Meganck, S., Leray, P., and Manderick, B. (2006a). Learning causal bayesian networks from observations and experiments: A decision theoritic approach. In Proceedings of the Third International Conference, MDAI 2006, volume 3885 of Lecture Notes in Artificial Intelligence, pages 58-69, Tarragona, Spain. Springer. [ DOI ]

[Meganck et al., 2006b]
Meganck, S., Maes, S., Leray, P., and Manderick, B. (2006b). Learning semi-markovian causal models using experiments. In The third European Workshop on Probabilistic Graphical Models PGM'06, pages 195-206, Prague, Czech Republic. [ .pdf ]

[Leray and Francois, 2005]
Leray, P. and Francois, O. (2005). Bayesian network structural learning and incomplete data. In Proceedings of the International and Interdisciplinary Conference on Adaptive Knowledge Representation and Reasoning (AKRR 2005), pages 33-40, Espoo, Finland. [ .pdf ]

[Meganck et al., 2005a]
Meganck, S., Maes, S., Leray, P., and Manderick, B. (2005a). A learning algorithm for multi-agent causal models. In Proceedings of the Third European Workshop on Multi-Agent Systems EUMAS 2005, pages 190-201, Bruxelles, Belgique.

[Meganck et al., 2005b]
Meganck, S., Maes, S., Manderick, B., and Leray, P. (2005b). Distributed learning of multi-agent causal models. In Proceedings of the 2005 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (IAT 2005), pages 285-288, Compiègne, France. [ DOI ]

[Grilheres et al., 2004]
Grilheres, B., Brunessaux, S., and Leray, P. (2004). Combining classifiers for harmful document filtering. In RIAO'2004, Coupling Approaches, Coupling Media and Coupling Languages for Information Retrieval, pages 173-185, Avignon, France. [ http ]

[Zaarour et al., 2004]
Zaarour, I., Leray, P., Heutte, L., Eter, B., Labiche, J., Mellier, D., and Zoater, M. (2004). Modelling of handwriting prototypes in graphonomics : bayesian network approach. In 5th EUROSIM Congress on Modelling and Simulation, EUROSIM'04, page (en CDROM), Marne-la-Vallée, France.

[Leray et al., 2003]
Leray, P., Zaarour, I., Heutte, L., Eter, B., Labiche, J., and Mellier, D. (2003). A bayesian model for discovering handwriting strategies of primary school children. In Working Notes of the Workshop on Probabilistic Graphical Models for Classification, ECML/PKDD-2003, pages 49-57, Cavtat-Dubrovnik, Croatia. [ .pdf ]

[Zaarour et al., 2003a]
Zaarour, I., Heutte, L., Eter, B., Labiche, J., Mellier, D., Leray, P., and Zoaeter, M. (2003a). A probabilistic modeling of the writing strategies evolution for pupils in primary education. In Teulings, H. and Van Gemmert, A., editors, 11th Conference of the International Graphonomics society (IGS 2003), pages 174-177, Scottsdale, Arizona, USA. [http ]

[Zaarour et al., 2003b]
Zaarour, I., Leray, P., Heutte, L., Eter, B., Labiche, J., and Mellier, D. (2003b). A bayesian network model for discovering handwriting strategies of primary school children. In Teulings, H. and Van Gemmert, A., editors, 11th Conference of the International Graphonomics society (IGS 2003), pages 178-181, Scottsdale, Arizona, USA. [ http ]

Former publications about neural networks and feature selection

[Leray and Gallinari, 1998]
Leray, P. and Gallinari, P. (1998). Data fusion for diagnosis in a telecommunication network. In Niklasson, L., Boden, M., and Ziemke, T., editors, 8th International Conference on Artificial Neural Networks (ICANN 98), pages 767-772, Skoevde, Sweden. Springer. [ .pdf ]

[Leray et al., 1997a]
Leray, P., Gallinari, P., and Didelet, E. (1997a). Local diagnosis for real-time network traffic management. In Third International Workshop on Applications of Neural Networks to Telecommunications (IWANNT'97), pages 124-130. Lawrence Erlbaum Associates, Publishers. [.pdf ]

[Leray et al., 1997b]
Leray, P., Gallinari, P., and Didelet, E. (1997b). Neural network tools for alarm generation in the telephone network traffic management. In DX'97 (Eight international workshop on principles of Diagnosis), pages 147-152. [.pdf ]

[Leray et al., 1996a]
Leray, P., Gallinari, P., and Didelet, E. (1996a). Diagnosis tools for telecommunication network traffic management. In Malsburg, C., von Seelen, W., Vorbrueggen, J., and Sendhoff, B., editors, 6th International Conference on Artificial Neural Networks (ICANN 96), pages 209-214, Bochum, Germany. Springer Verlag. [.pdf ]

[Leray et al., 1996b]
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