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 |
|