Antoine Cornuéjols
Publications
Publications
Bottom-up assembly of beneficial multi-species biofilms targeting undesirable bacteria using 3D fluorescence imagingXXX Congreso Sociedad Espanola de Microbiologia, SEM, Jun 2025, Jaen, Spain |
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Augmentation par Rééchantillonnage pour l'Apprentissage Contrastif de Séries Temporelles : Application à la TélédétectionORASIS 2025, ISEN Yncréa Ouest, Jun 2025, Le Croisic, France |
Live fluorescence confocal imaging to study microbial interaction in multi-species biofilmsJournée annuelle France BioImaging IDF Sud, France BioImaging, Jun 2025, Jouy-en-Josas, France |
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Resampling Augmentation for Time Series Contrastive Learning: Application to Remote SensingProceedings of TerraBytes: Towards global datasets and models for Earth Observation Workshop at the 42nd International Conference on Machine Learning, Jul 2025, Vancouver, BC, Canada, Canada |
Estimating the Learning Capacity of Bacterial Metabolic NetworksAdvances in Intelligent Data Analysis XXIII. IDA 2025, May 2025, Konstanz, Germany, Germany. pp.28-40, ⟨10.1007/978-3-031-91398-3_3⟩ |
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Learning from few labeled time series with segment-based self-supervised learning: application to remote-sensingRencontres de la Société Francophone de Classification, Pascal Préa, Sep 2024, Marseille (CIRM, Centre International de Rencontres Mathématiques), France |
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Coaching Agent: Making Recommendations for Behavior Change. A Case Study on Improving Eating HabitsAAMAS ' 22: International Conference on Autonomous Agents and Multi-Agent Systems, ACM - Association for Computing Machinery, May 2022, Virtual Event New Zealand, New Zealand. pp.1292-1300 |
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Coaching Agent: Making Recommendations for Behavior Change. A Case Study on Improving Eating Habits21st International Conference on Autonomous Agents and Multiagent Systems, International Foundation for Autonomous Agents and Multiagent Systems, May 2022, Online, France. pp.1292-1300 |
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When to Classify Events in Open Times Series?Asian Conference on Machine Learning (ACML), Dec 2022, Hyderabad, India |
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Early and Revocable Time Series ClassificationInternational Joint Conference on Neural Networks (IJCNN), Jul 2022, Padua, Italy. ⟨10.1109/IJCNN55064.2022.9892391⟩ |
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Repondération Préférentielle pour l'Apprentissage BiqualitéConférence Francophone sur l'Extraction et la Gestion des Connaissances (EGC-2022), Jan 2022, Blois, France. pp.339-346 |
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Le coaching : un nouveau cadre pour la recommandation automatique en vue de modifications durables du comportementCNIA 2021 : Conférence Nationale en Intelligence Artificielle, 2021, Bordeaux, France. pp.44-51 |
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From Weakly Supervised Learning to Biquality Learning: an Introduction2021 International Joint Conference on Neural Networks (IJCNN), Jul 2021, Shenzhen, China. pp.1-10, ⟨10.1109/IJCNN52387.2021.9533353⟩ |
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Early Classification of Time Series: Cost-based multiclass Algorithms2021 IEEE 8th International Conference on Data Science and Advanced Analytics (DSAA), Oct 2021, Porto, Portugal. pp.1-10, ⟨10.1109/DSAA53316.2021.9564134⟩ |
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Using Agents and Unsupervised Learning for Counting Objects in Images with Spatial Organization13th International Conference on Agents and Artificial Intelligence, Feb 2021, Online Streaming, Austria. pp.688-697, ⟨10.5220/0010228706880697⟩ |
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Contrastive Representations for Label Noise Require Fine-TuningWorkshop IAL-2021 (Interactive Adaptive Learning Workshop) in ECML-PKDD-2021, Sep 2021, Bilbao, Spain, Spain |
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Importance Reweighting for Biquality Learning2021 International Joint Conference on Neural Networks (IJCNN), Jul 2021, Shenzhen, China. pp.1-8, ⟨10.1109/IJCNN52387.2021.9533349⟩ |
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Transfer Learning by Learning Projections from Target to SourceInternational Symposium on Intelligent Data Analysis, Apr 2020, Constance, Germany. pp.119 - 131, ⟨10.1007/978-3-030-44584-3_10⟩ |
Solving analogies on words based on minimal complexity transformationInternational Joint Conference on Artificial Intelligence (IJCAI-2020), Jan 2021, Kyoto, Japan. pp.1848-1854 |
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Predictive K-means with local modelsInternational Joint Conference on Neural Networks (IJCNN) 2020, Jul 2020, Glasgow, United Kingdom |
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Exploring eating behaviours modelling for user clusteringHealthRecSys@RecSys 2018 colocated with ACM Recsys’18 (ACM Conference Series on Recommender Systems), Oct 2018, Vancouver, Canada. pp.46-51 |
Online Learning with Reoccurring Drifts: The Perspective of Case-Based ReasoningThird Workshop on Synergies between CBR and Data Mining, ICCBR-2018, 2018, Stockholm, Sweden |
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An Information Theory based Approach to Multisource ClusteringTwenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}, Jul 2018, Stockholm, Sweden. ⟨10.24963/ijcai.2018/358⟩ |
Adaptive Window Strategy for Topic Modeling in Document StreamsInternational Joint Conference on Neural Networks, 2018, Rio de Janeiro, Brazil |
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Adaptive collaborative topic modeling for online recommendation12th ACM Conference on Recommender Systems (RecSys 2018), Oct 2018, Vancouver, Canada. 9 p., ⟨10.1145/3240323.3240363⟩ |
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Triclustering based outlier-shape score for time series in a fraud detection platformAALTD'18, 3nd ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data, Sep 2018, Dublin, Ireland |
Opening the parallelogram: Considerations on non-Euclidean analogiesInternational Conference on Case-Based Reasoning (ICCBR 2018), Jul 2018, Stockholm, Sweden |
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Incremental learning with the minimum description length principle2017 International Joint Conference on Neural Networks (IJCNN-2017), May 2017, Anchorage, United States |
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Collaborative clustering based on Algorithmic Information TheoryCAp ( Conférence sur l'Apprentissage Automatique ) 2017, Jun 2017, Grenoble, France |
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Data Collection and Analysis of Usages from Connected Objects: Some LessonsInternational Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE, Jun 2017, Arras, France. pp.251-258, ⟨10.1007/978-3-319-60045-1_27⟩ |
A complexity based approach for solving Hofstadter's analogiesInternational Conference on Case-Based Reasoning (ICCBR 2017), Jul 2017, Trondheim, Northern Mariana Islands. pp.53-62 |
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A complexity based approach for solving Hofstadter’s analogiesCAW@ICCBR-2017 Computational Analogy Workshop, at International Conference on Case Based Reasoning, Jun 2017, Trondheim, Norway |
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Clustering collaboratif : Principes et mise en oeuvreBDA (Gestion de Données - Principes, Technologies et Applications), Nov 2017, Nancy, France |
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Investigating substitutability of food items in consumption dataSecond International Workshop on Health Recommender Systems co-located with ACM RecSys, Aug 2017, Como, Italy |
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Un critère d'évaluation pour les K-moyennes prédictivesEGC-2017 Conférence Extraction et Gestion des Connaissances, Jan 2017, Grenoble, France |
Minimum description length principle applied to structure adaptation for classification under concept drift2016 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), Jul 2016, Vancouver, Canada. ⟨10.1109/IJCNN.2016.7727558⟩ |
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A novel algorithm for online classification of time series when delaying decision is costlyCAP'2016 Conférence francophone sur l'Apprentissage Automatique, Jul 2016, Marseille, France |
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Evaluation of predictive clustering qualityWorkshop on Model-Based Clustering and Classification, 2016, Catania, Italy |
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Une méthode supervisée pour initialiser les centres des K-moyennes16. Conférence Internationale Francophone sur l' Extraction et Gestion des Connaissances (EGC 2016), Jan 2016, Reims, France |
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Collaborative learning using topographic mapsAAFD and SFC'16 Conférence Internationale Francophone "Science des données. Défis Mathématiques et algorithmiques", May 2016, Marrakech, Morocco |
Vertical collaborative clustering using generative topographic maps7th International Conference of Soft Computing and Pattern Recognition (SoCPaR), Nov 2015, Fukuoka, Japan. pp.199-204, ⟨10.1109/SOCPAR.2015.7492807⟩ |
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Collaborative-Based Multi-scale Clustering in Very High Resolution Satellite ImagesInternational Conference on Neural Information (ICONIP) Processing, Oct 2016, Tokyo, Japan. pp.148-155, ⟨10.1007/978-3-319-46675-0_17⟩ |
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Denoising 3D microscopy images of cell nuclei using shape priors on an anisotropic gridICPRAM 2016 International Conference on Pattern Recognition Applications and Method, Feb 2016, Rome, Italy |
Apprentissage du signal prix de l'électricité. Arbres de régression, séries temporelles et prédictions à long terme16e Conférence Francophone Extraction et Gestion des Connaissances (EGC 2016), Jan 2016, Reims, France. pp.543-544 |
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An initialization scheme for supervized K-meansIJCNN 2015 International Joint Conference on Neural Networks, Jul 2015, Killarney, Ireland. ⟨10.1109/IJCNN.2015.7280555⟩ |
Semantic rich ICM algorithm for VHR satellite images segmentation14th IAPR International Conference on Machine Vision Applications (MVA), May 2015, Tokyo, Japan. pp.15292961, ⟨10.1109/MVA.2015.7153129⟩ |
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Initialisation des k-moyennes à l’aide d’une décomposition supervisée des classesSFC-2015 Congrès de la Société Française de Classification, Sep 2015, Nantes, France |
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Classification à base de clustering ou décrire et prédire simultanémentTreizièmes Rencontres des Jeunes Chercheurs en Intelligence Artificielle (RJCIA 2015), Jun 2015, Rennes, France. 6 p |
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Symbolic representation of time series : a hierarchical coclustering formalizationWorkshop "Advanced Analytics and Learning on Temporal Data ", ECML-PKDD-2015, Sep 2015, porto, Portugal |
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Early classification of time series as a non myopic sequential decision making problemEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2015), Sep 2015, Porto, Portugal. ⟨10.1007/978-3-319-23528-8_27⟩ |
Collaborative Clustering with Heterogeneous AlgorithmsInternational Joint Conference on Neural Networks (IJCNN), Jul 2015, Killarney, Ireland. 8 p |
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Collaborative Clustering with Heterogeneous AlgorithmsIJCNN 2015, Jul 2015, Killarney, Ireland. pp.8 |
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Mesure de qualité de la classification à base des K-moyennesJournée Clustering, Oct 2015, Issy-les-Moulineaux, France |
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Clustering collaboratif incrémentalConférence Francophone d'Apprentissage ( CAP2014 ), Jul 2014, Saint Etienne, France |
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Un nouveau modèle d’énergie pour les champs aléatoires de Markov cachés21. Rencontres de la Société Francophone de Classification SFC'14, 2014, Rabat, Maroc |
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A new energy model for the Hidden Markov Random Fields21. International Conference on Neural Information Processing (ICONIP 2014), Nov 2014, kuching, Malaysia |
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A supervised methodology to measure the variables contribution to a clustering21. International Conference on Neural Information Processing (ICONIP 2014), Nov 2014, kuching, Malaysia |
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Unsupervised one class identification by selecting and combining ranking functionsCAP' 2014 Conférence francophone sur l'Apprentissage Automatique, Oct 2014, Saint-Etienne, France |
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Recherche de documents par apprentissage artificial pour une méta-analyse sur les emissions d’un gaz à effet de serre19. Congrès national sur la Reconnaissance de Formes et Intelligence Artificielle (RFIA' 14), Jun 2014, Rouen, France |
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Evaluation protocol of early classifiers over multiple data sets21. International Conference on Neural Information Processing (ICONIP 2014), Nov 2014, Kuching, Malaysia |
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An Ensemble Method for Unsupervised One Class IdentificationSFC-2014 (21ème rencontres de la Société Francophone de Classification), Sep 2014, Rabat, Morocco |
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Supervised pretreatments are useful for supervised clusteringEuropean Conference on Data Analysis ( ECDA 2014 ), Jul 2014, Bremen, Germany |
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An ontology of scientific uncertainty : methodological lessons from analyzing expressions of uncertainty in food risk assessment2013: ISA/ESA Mid Term Conference, Jan 2013, Amsterdam, Netherlands |
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Anticipative and dynamic adaptation to concept changesEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013), Sep 2013, Prague, France |
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Anticipative and Dynamic Adaptation to Concept ChangesCAp2013 : Conférence sur l'APprentissage automatique, Jul 2013, Villeneuve d'Ascq, France |
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Early classification of individual electricity consumptionsEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD 2013), Sep 2013, Prague, France |
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A new on-line learning method for coping with recurring concepts: the ADACC system20. International Conference on Neural Information Processing (ICONIP 2013), Nov 2013, Daegu, South Korea. pp.595-604, ⟨10.1007/978-3-642-42042-9_74⟩ |
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An ontology of scientific uncertainty : methodological lessons from analyzing expressions of uncertainty in food risk assessment4S 2013 San Diego : Society for Social Studies of Science, Oct 2013, San Diego, United States |
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Online learning : searching for the best forgetting strategy under concept drift20. International Conference on Neural Information Processing (ICONIP 2013), Nov 2013, Daegu, South Korea. pp.400-408, ⟨10.1007/978-3-642-42042-9_50⟩ |
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Apprentissage artificiel : mise en perspective d'un demi-siècle d'évolutionConférence Extraction et Gestion des Connaissance (EGC-2012), Jan 2012, Bordeaux, France |
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Une nouvelle méthode de combinaison d'outils d'identification non supervisésAtelier PROSPECTOM, Nov 2012, Grenoble, France |
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Détection non supervisée d'une sous-population par méthode d'ensemble et changement de représentation itératif12. Conférence Internationale Francophone sur l'Extraction et la Gestion des Connaissances (EGC 2012), Jan 2012, Bordeaux, France |
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Reacting to concept changes using a committee of experts12. Conférence Internationale Francophone sur l'Extraction et la Gestion des Connaissances (EGC 2012), Jan 2012, Bordeaux, France |
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Apprentissage artificiel : mise en perspective d'un demi-siècle d'évolution.5èmes Journées Apprentissage Artificiel et Fouille de Données, Jun 2012, Univerrsité Paris 13 Villetaneuse France |
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Using reliable and surprising item sets for the characterization of protein-protein interfacesJOBIM 2009 : 10. Journées Ouvertes Biologie Informatique Mathématiques, Jun 2009, Nantes, France. n.p |
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Comment optimiser A* adaptatifRFIA 2008: 16.Congrès Francophone AFRIF-AFIA. Reconnaissance des Formes et Intelligence Artificielle, Jan 2008, Amiens, France. 8 p |
Identifying interface elements implied in protein-protein interactions using statistical tests and frequent item setsIEEE BIBM 2008, Nov 2008, Philadelphia, United States. ⟨10.1109/BIBM.2008.68⟩ |
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What is the place of Machine Learning between Pattern Recognition and Optimization?TML 2008 Conference: (Teaching Machine Learning), May 2008, Saint Etienne, France. 6 p |
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Relevant features mining on protein-protein interfacesMeta'08 : International Conference on Metaheuristics and Nature Inspired Computing, Oct 2008, Hammamet, Tunisia. 2 p |
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Comment optimiser A∗ adaptatif / Adaptive A∗ : how to best exploit past problem-solving episodesReconnaissance des Formes et Intelligence Artificielle, Jan 2008, Amiens, France |
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A Phase TRansition-Based Perspective on Multiple Instance KernelsILP 2007, Jude Shavlik, Hendrik Blockeel, Prasad Tadepalli, Jun 2007, Corvallis, United States |
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A Phase Transition-based Perspective on Multiple Instance KernelsConférence francophone sur l'apprentissage automatique, Jean-Daniel Zucker, Antoine Cornuéjols, Jul 2007, Grenoble, France. pp.173--186 |
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A phase transition-based perspective on multiple instance kernelsPlate-forme AFIA 2007: Conférence francophone sur l'Apprentissage automatique, Jul 2007, Grenoble, France. ⟨10.1007/978-3-540-78469-2_14⟩ |
Combining feature ranking methods for high dimensional data analysis5th Workshop on Statistical Methods for Post-Genomic Data, Jan 2007, Paris, France |
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Phase Transitions within Grammatical InferenceInt. Joint Conf. on Artificial Intelligence (IJCAI), Jul 2005, Edinburgh, United Kingdom |
Artificial data and language theoryGI workshop 2006. Grammatical inference: workshop on open problems and new directions, Colin de la Higuera, Nov 2005, Saint-Etienne, France |
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Ensemble Feature RankingECML-PKDD, Sep 2004, Pisa, Italy |
Determination of cellular drug targets: searching for functional information in the jungle of microarrays dataCurrent trends in drug discovery research" (CTDDR-2004), Feb 2004, Lucknow, India |
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Tunnel effects" in cognition : A transfer mechanism from known conceptual domains to new onesConference on Artificial Intelligence and Simulation of Behaviour (AISB-99), Apr 1999, Edinburgh, United Kingdom |
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A new mechanism for transfer between conceptual domains in scientific discovery and education1st Conference on Model-Based Reasoning (MBR-98), Dec 1998, Pavia, Italy |
Cognitive hints for analogy modellingProceedings of the European Conference on Machine Learning (ECML-98), Workshop on Learning in Humans and Machines, Apr 1998, Chemnitz, Germany |
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Analogy and Induction : which (missing) link ?Workshop "Advances in Analogy Research : Integration of Theory and Data from Cognitive, Computational and Neural Sciences", Jul 1998, Sofia, Bulgaria |
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Co-Adaptation of Students' Knowledge Domains when Interpreting a Physical Situation in Terms of a New Theory2nd European Conference on Cognitive Science (ECCS-97), Apr 1997, Manchester, United Kingdom |
Analogy as a description minimization principleWorkshop on "Applications of Descriptional Complexity to Inductive, Statistical, and Visual Inference" ML/COLT-95, Jul 1994, Rutgers, United States |
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Analogy as a minimization principleDagstuhl Seminar : "Theory and Praxis of Machine Learning", Jun 1994, Dagstuhl, Germany |
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Induction from one example and statistics : Analogy as a minimization principleWorkshop on "Machine Learning and Statistics", ECML-94 (European Conf. on Mach. Learning), Apr 1994, Catanes, Italy |
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Memory Limitations and Optimisation of Training Sequences for Incremental LearnersMLnet workshop on Learning in Autonomous Agents, Sep 1993, Blanes, Spain |
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Getting Order Independence in Incremental LearningEuropean Conference on Machine Learning (ECML-93), Apr 1993, Vienne, Austria |
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An Exploration into Incremental Learning: the INFLUENCE system6th International Workshop on Machine Learning (ICML-89), Jun 1989, Cornell, United States |
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Statistical filtering of motion field from image sequencesGRETSI-87, Jun 1987, Nice, France |
Revision of interpretation in Episodic Memory by using Chemistry instead of Reason Maintenance SystemsMARI-87 (MAchine et Réseaux Intelligents – Cognitiva-87), May 1987, Paris, France |
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Conférence Nationale d’Intelligence Artificielle Année 2025Association Française pour l'Intelligence Artificielle, 2025 |
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Advances in Knowledge Discovery and ManagementSpringer Nature Switzerland, 1110, 2024, Studies in Computational Intelligence, 9783031404023. ⟨10.1007/978-3-031-40403-0⟩ |
Artificial Intelligence. What is it, exactly?Sébastien Konieczny; Henri Prade. College Publication, 2021, 9781848903388 |
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Apprentissage artificiel - 4e éditionEyrolles, 990 p., 2021, Algorithmes, 978-2-416-00104-8 |
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L’intelligence Artificielle: De quoi s’agit-il vraiment ?Sébastien Konieczny; Henri Prade. Cepadues, 2020, 9782364938502 |
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. Apprentissage artificiel - 3e édition: Deep Learning, Concepts et algorithmesEyrolles, 900 p., 2018, Algorithmes, 978-2-212-67522-1 |
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Phase transitions in machine learningCambridge University Press, 2011, 978-0521763912 |
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Apprentissage artificiel : Concepts et algorithmesEyrolles, pp.804, 2010, 978-2-212-12471-2 |
Design of antagonistic biofilm communities for pathogen exclusionASM Biofilms, Nov 2025, Portland (OR), United States. 2025 |
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Development of Artificial Intelligence methods for behavior characterization and automatic lameness detection in dairy cowsJournées d'animation scientifique du département Santé Animale (JAS), Sep 2024, Seignosse 'Les Tuquets', France. , pp.28, 2024, Journées d'animation scientifique du département Santé Animale (JAS) |
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Evaluation of information processing capacity of bacterial metabolism through regression problem solvingBioSynSys 2023 - Symposium on Synthetic and Systems Biology, Jul 2023, Toulouse, France |
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Solving regression problems using bacterial metabolism2023 Living Machines @ Work day, Oct 2023, Gif-sur-Yvette, France. |
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Neural-like computation using bacterial metabolism to solve machine-learning problemsDIM BioConvS - Innovation Day, Nov 2023, St-Ouen, France |
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Early Classification of Time Series is Meaningful2025 |
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Biquality Learning: a Framework to Design Algorithms Dealing with Closed-Set Distribution Shifts2023 |
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From Shallow to Deep Interactions Between Knowledge Representation, Reasoning and Machine Learning (Kay R. Amel group)2019 |
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Advances in Knowledge Discovery and ManagementConférence Francophone sur l'Extraction et la Gestion des Connaissances (EGC-2022), 1110, Springer Nature Switzerland, 2024, Studies in Computational Intelligence, ⟨10.1007/978-3-031-40403-0⟩ |
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Predictive K-means with Local ModelsTrends and Applications in Knowledge Discovery and Data Mining (PAKDD 2020 workshops), 12237, Springer International Publishing, pp.91-103, 2020, Lecture Notes in Computer Science, ⟨10.1007/978-3-030-60470-7_10⟩ |
Training Issues in Incremental LearningAAAI Spring Symposium on "Training Issues in Incremental Learning", Mar 1993, Stanford, United States. 1993, Technical Report SS-93-06. Published by The AAAI Press, 978-0-929280-44-8 |
Designing Algorithms for Machine Learning and Data MiningA Guided Tour of Artificial Intelligence Research, Springer International Publishing, pp.339-410, 2020, 978-3-030-06166-1. ⟨10.1007/978-3-030-06167-8_12⟩ |
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Supervised pre-processings are useful for supervised clusteringStudies in classification, data analysis, and knowledge organization, Editions Springer, 2015, Studies in Classification, Data Analysis, and Knowledge Organization, 978-3-319-25226-1 ; 978-3-319-25224-7. ⟨10.1007/978-3-319-25226-1_13⟩ |
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Paradigmes de l’apprentissage artificielPanorama de l'intelligence artificielle - ses bases méthodologiques, ses développements, Cepaduès Editions, 2014 |
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Algorithmique de l’apprentissage artificiel et de la fouille de données.Pierre Marquis, Odile Papini, Henri Prade. Panorama de l'Intelligence Artificielle, 2, Cépaduès Edition, 2014, Algorithmes pour l'intelligence artificielle |
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Unsupervised object ranking using not even weak expertsNeural Information Processing 2011: Proceedings, 18th International Conference, ICONIP 2011 Shanghai, China, November 13-17, 2011 Proceedings, Part I, 7062, Springer - Verlag, 2011, Lecture Notes in Computer Science, 978-3-642-24954-9. ⟨10.1007/978-3-642-24955-6_72⟩ |
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Predicting Concept Changes Using a Committee of ExpertsNeural Information Processing: 18th International Conference, ICONIP 2011 Shanghai, China, November 13-17, 2011 Proceedings, Part I, 7062, Springer - Verlag, 2011, Lecture Notes in Computer Science, 978-3-642-24954-9. ⟨10.1007/978-3-642-24955-6_69⟩ |
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Resource-aware distributed knowledge discoveryMichael May; Lorenza Saitta. Ubiquitous Knowledge Discovery, 6202, Springer, 2010, Lecture Notes in Computer Science, 978-3-642-16391-3. ⟨10.1007/978-3-642-16392-0_3⟩ |
On-Line Learning: Where are we so Far?Ubiquitous Knowledge Discovery Challenges, Techniques, Applications, Springer - Verlag, 2010, 978-3-642-16391-3 |
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Research Challenges in Ubiquitous Knowledge DiscoveryKargupta and Han and Yu and Motwani and Kumar. Next Generation of Data Mining, CRC Press, pp.131-151, 2009, Next Generation of Data Mining |
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Machine Learning: The Necessity of Order (is order in order ?)F. Ritter, J. Nerb, E. Lehtinen & T. O'Shea (Eds.). In order to learn: How the sequences of topics affect learning, Oxford University Press, 2006 |
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Using an attribute estimation technique for the analysis of microarray dataP. Amar, F. Képès, V. Norris and P. Tracqui. Proc. of the Dieppe Spring school on Modelling and simulation of biological processes in the context of genomics, Publisher Frontier group, 2003, Proc. of the Dieppe Spring school on Modelling and simulation of biological processes in the context of genomics, 2 91 4601 09 3 |
Évolution des connaissances chez l'apprenantDes connaissances naïves au savoir scientifique, pp.31-50, 2003, Des connaissances naïves au savoir scientifique |
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Machine Learning : A SurveySpyros G. Tzafestas. Knowledge Based Systems. Advanced Concepts, Techniques and Applications, World Scientific, pp.61-86, 1997, 978-9810228309 |
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Analogy as Minimization of Description LengthNakhaeizadeh, G. and Taylor, C. Machine Learning and Statistics : The interface, John Wiley & Sons, pp.321-335, 1996, 978-0471148906 |