Alex Aussem

According to our database1, Alex Aussem authored at least 72 papers between 1995 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2021
Neural Embedded Dirichlet Processes for Topic Modeling.
Proceedings of the Modeling Decisions for Artificial Intelligence, 2021

Data-Efficient Information Extraction from Documents with Pre-trained Language Models.
Proceedings of the Document Analysis and Recognition, 2021

2020
Building bagging on critical instances.
Expert Syst. J. Knowl. Eng., 2020

End-to-End Extraction of Structured Information from Business Documents with Pointer-Generator Networks.
Proceedings of the Fourth Workshop on Structured Prediction for NLP@EMNLP 2020, 2020

2019
Hierarchical Recurrent Attention Networks for Context-Aware Education Chatbots.
Proceedings of the International Joint Conference on Neural Networks, 2019

Recurrent Neural Network Approach for Table Field Extraction in Business Documents.
Proceedings of the 2019 International Conference on Document Analysis and Recognition, 2019

2018
On the use of binary stochastic autoencoders for multi-label classification under the zero-one loss.
Proceedings of the INNS Conference on Big Data and Deep Learning 2018, 2018

2017
Dynamic Ensemble Selection with Probabilistic Classifier Chains.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Reducing variance due to importance weighting in covariate shift bias correction.
Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

2016
An extensive empirical comparison of ensemble learning methods for binary classification.
Pattern Anal. Appl., 2016

Ensemble multi-label text categorization based on rotation forest and latent semantic indexing.
Expert Syst. Appl., 2016

F-Measure Maximization in Multi-Label Classification with Conditionally Independent Label Subsets.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2016

Identifying the irreducible disjoint factors of a multivariate probability distribution.
Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

Similarity Tree Pruning: A Novel Dynamic Ensemble Selection Approach.
Proceedings of the IEEE International Conference on Data Mining Workshops, 2016

A Semi-Supervised Ensemble Approach for Multi-label Learning.
Proceedings of the IEEE International Conference on Data Mining Workshops, 2016

2015
Unsupervised feature selection with ensemble learning.
Mach. Learn., 2015

A Practical Approach to Reduce the Learning Bias Under Covariate Shift.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2015

Ensemble Multi-label Classification: A Comparative Study on Threshold Selection and Voting Methods.
Proceedings of the 27th IEEE International Conference on Tools with Artificial Intelligence, 2015

Correcting a Class of Complete Selection Bias with External Data Based on Importance Weight Estimation.
Proceedings of the Neural Information Processing - 22nd International Conference, 2015

Calibrated k-labelsets for Ensemble Multi-label Classification.
Proceedings of the Neural Information Processing - 22nd International Conference, 2015

On the Optimality of Multi-Label Classification under Subset Zero-One Loss for Distributions Satisfying the Composition Property.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
A hybrid algorithm for Bayesian network structure learning with application to multi-label learning.
Expert Syst. Appl., 2014

Analysis of risk factors of hip fracture with causal Bayesian networks.
Proceedings of the International Work-Conference on Bioinformatics and Biomedical Engineering, 2014

A Comparison of Multi-Label Feature Selection Methods Using the Random Forest Paradigm.
Proceedings of the Advances in Artificial Intelligence, 2014

2013
Learning the local Bayesian network structure around the ZNF217 oncogene in breast tumours.
Comput. Biol. Medicine, 2013

2012
A semi-supervised feature ranking method with ensemble learning.
Pattern Recognit. Lett., 2012

Analysis of nasopharyngeal carcinoma risk factors with Bayesian networks.
Artif. Intell. Medicine, 2012

An Experimental Comparison of Hybrid Algorithms for Bayesian Network Structure Learning.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

2011
Trading-Off Diversity and Accuracy for Optimal Ensemble Tree Selection in Random Forests.
Proceedings of the Ensembles in Machine Learning Applications, 2011

Semi-supervised Feature Importance Evaluation with Ensemble Learning.
Proceedings of the 11th IEEE International Conference on Data Mining, 2011

2010
A novel Markov boundary based feature subset selection algorithm.
Neurocomputing, 2010

A conservative feature subset selection algorithm with missing data.
Neurocomputing, 2010

Bayesian networks.
Neurocomputing, 2010

Analysis of lifestyle and metabolic predictors of visceral obesity with Bayesian Networks.
BMC Bioinform., 2010

An Efficient and Scalable Algorithm for Local Bayesian Network Structure Discovery.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Feature Selection for Unsupervised Learning Using Random Cluster Ensembles.
Proceedings of the ICDM 2010, 2010

Une approche de co-classification automatique à base des cartes topologiques.
Proceedings of the Apprentissage Artificiel et Fouille de Données, 2010

2009
Incremental Bayesian Network Learning for Scalable Feature Selection.
Proceedings of the Advances in Intelligent Data Analysis VIII, 2009

Exploiting Data Missingness in Bayesian Network Modeling.
Proceedings of the Advances in Intelligent Data Analysis VIII, 2009

Graph-Based Analysis of Nasopharyngeal Carcinoma with Bayesian Network Learning Methods.
Proceedings of the Graph-Based Representations in Pattern Recognition, 2009

Robust Gene Selection from Microarray Data with a Novel Markov Boundary Learning Method: Application to Diabetes Analysis.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2009

2008
A Novel Scalable and Data Efficient Feature Subset Selection Algorithm.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2008

Intégration de contraintes dans les cartes auto-organisatrices.
Proceedings of the Extraction et gestion des connaissances (EGC'2008), 2008

Handling almost-deterministic relationships in constraint-based Bayesian network discovery : Application to cancer risk factor identification.
Proceedings of the ESANN 2008, 2008

SOM based clustering with instance-level constraints.
Proceedings of the ESANN 2008, 2008

2007
Analysis of Nasopharyngeal Carcinoma Data with a Novel Bayesian Network Learning Algorithm.
Proceedings of the 2007 IEEE International Conference on Research, 2007

Learning Bayesian network structures by estimation of distribution algorithms: An experimental analysis.
Proceedings of the Second IEEE International Conference on Digital Information Management (ICDIM), 2007

A novel Bayesian Network structure learning algorithm based on minimal correlated itemset mining techniques.
Proceedings of the Second IEEE International Conference on Digital Information Management (ICDIM), 2007

Approche connexionniste pour l'extraction de profils cas-témoins du cancer du Nasopharynx à partir des données issues d'une étude épidémiologique.
Proceedings of the Extraction et gestion des connaissances (EGC'2007), 2007

Application des réseaux bayésiens à l'analyse des facteurs impliqués dans le cancer du Nasopharynx.
Proceedings of the Extraction et gestion des connaissances (EGC'2007), 2007

Nasopharyngeal Carcinoma Data Analysis with a Novel Bayesian Network Skeleton Learning Algorithm.
Proceedings of the Artificial Intelligence in Medicine, 2007

2006
Apprentissage de la structure des réseaux bayésiens à partir des motifs fréquents corrélés : application à l'identification des facteurs environnementaux du cancer du Nasopharynx.
Proceedings of the Extraction et gestion des connaissances (EGC'2006), 2006

Probabilistic classifiers and time-scale representations: application to the monitoring of a tramway guiding system.
Proceedings of the ESANN 2006, 2006

Learning with monotonicity requirements for optimal routing with end-to-end quality of service constraints.
Proceedings of the ESANN 2006, 2006

Modelling switching dynamics using prediction experts operating on distinct wavelet scales.
Proceedings of the ESANN 2006, 2006

2003
Distributed Neural Networks for Quality of Service Estimation in Communication Networks.
Int. J. Comput. Intell. Appl., 2003

Closed Loop Stability of FIR-Recurrent Neural Networks.
Proceedings of the Artificial Neural Networks and Neural Information Processing, 2003

2002
Sufficient Conditions for Error Backflow Convergence in Dynamical Recurrent Neural Networks.
Neural Comput., 2002

e-functional dependency inference: application to DNA microarray expression data.
Proceedings of the 18èmes Journées Bases de Données Avancées, 2002

Le Calcul du Gradient d'Erreur dans les Réseaux de Neurones : Applications aux Telecom et aux Sciences Environnementales.
, 2002

2001
Modeling the Ultimate Seaweed Expansion.
Simul., 2001

Web traffic demand forecasting using wavelet-based multiscale decomposition.
Int. J. Intell. Syst., 2001

Segmentation of switching dynamics with a Hidden Markov Model of neural prediction experts.
Proceedings of the ESANN 2001, 2001

2000
Neural network modelling for environmental prediction.
Neurocomputing, 2000

Neural-network metamodelling for the prediction of Caulerpa taxifolia development in the Mediterranean sea.
Neurocomputing, 2000

Queuing Network Modeling with Distributed Neural Networks for Service Quality Estimation in B-ISDN Network.
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

Sufficient Conditions for Error Back Flow Convergence in Dynamical Recurrent Neural Networks.
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, 2000

1999
Dynamical recurrent neural networks towards prediction and modeling of dynamical systems.
Neurocomputing, 1999

1998
Data Imputation and Nowcasting in the Environmental Sciences Using Clustering and Connectionist Modelling.
Proceedings of the COMPSTAT 1998, 1998

1997
Combining Neural Network Forecasts on Wavelet-transformed Time Series.
Connect. Sci., 1997

1996
Fuzzy astronomical seeing nowcasts with a dynamical and recurrent connectionist network.
Neurocomputing, 1996

1995
Dynamical recurrent neural networks -- towards environmental time series prediction.
Int. J. Neural Syst., 1995


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