Research area

The team Machine Learning and Applications (A³) tackles machine learning problems by using supervised, unsupervised and hybrid methods. This research is fed, coordinated and evaluated thanks to various applications in the field of the pattern recognition and data mining.

Statistical Learning Group

Topic 1 : Clustering and data visualization

• Two-levels clustering
• Mixture models clustering

Topic 2 : Generalization control and adjustement

• Feature transformation, extraction and selection
• Model combination and decisions fusion
• Clustering stability

Topic 3 : Knowledge extraction from complex data

• Numerical learning from structured data
• Evolving data modelisation
• Multi-modal data analysis and exploration

Symbolic Learning Group

Topic 1 : Supervised symbolic learning

• Relationnal learning
• Search strategies in symbolic learning
• Propositionalisation (Datalog to propositionnal and multi-instance)
• Learning from incomplete data

Topic 2 : Reinforcement and incremental learning

• Relational Reinforcement Learning
• Collective Learning
• CBR and Learning of Relational Metrics

Topic 3 : Exploratory analysis of symbolic data

• Learning in Social and Biological Networks
• Association rules extraction and selection
• Genes interactions network extraction from texts

Last modified: Wednesday 28 July 2010

Contact for this webpage: Sebastien.Guerif at lipn.univ-paris13.fr