The Flowers team studies computational mechanisms allowing robots and humans to acquire openended repertoires of skills through life-long learning. This includes the processes for progressively discovering their bodies and interaction with objects, tools and others. In particular, we study mechanisms of intrinsically motivated learning (also called curiosity-driven active learning), autonomous ...

In manipulating data such as in supervised or unsupervised learning, we need to extract new features from the original features for the purpose of reducing the dimension of feature space and achieving better performance. In this paper, we investigate a novel schema for unsupervised feature extraction for classification problems. We based our method on clustering to achieve feature extraction. A...

This paper describes a novel feature selection algorithm for unsupervised clustering, that combines the clustering ensembles method and the population based incremental learning algorithm. The main idea of the proposed unsupervised feature selection algorithm is to search for a subset of all features such that the clustering algorithm trained on this feature subset can achieve the most similar ...

Many machine learning algorithms assume that all input samples are independently and identically distributed from some common distribution on either the input space X, in the case of unsupervised learning, or the input and output space X x Y in the case of supervised and semi-supervised learning. In the last number of years the relaxation of this assumption has been explored and the importance ...

Dynamical systems are mathematical models expressing cause-e ect relations of time-varying phenomena. This thesis focuses on learning dynamical systems from empirical observations. Three settings are considered: unsupervised, supervised, and active learning. The unifying goal is to extract predictive information from data. A method is introduced to cluster time-series and perform model validati...

The Flowers team studies computational mechanisms allowing robots and humans to acquire openended repertoires of skills through life-long learning. This includes the processes for progressively discovering their bodies and interaction with objects, tools and others. In particular, we study mechanisms of intrinsically motivated learning (also called curiosity-driven active learning), autonomous ...

A propos du centre ou de la direction fonctionnelle The Flowers team studies computational mechanisms allowing robots and humans to acquire open-ended repertoires of skills through life-long learning. This includes the processes for progressively discovering their bodies and interaction with objects, tools and others. In particular, we study mechanisms of intrinsically motivated learning (also ...

The Flowers team studies computational mechanisms allowing robots and humans to acquire openended repertoires of skills through life-long learning. This includes the processes for progressively discovering their bodies and interaction with objects, tools and others. In particular, we study mechanisms of intrinsically motivated learning (also called curiosity-driven active learning), autonomous ...