Using request radio shows as a base interactive model, we present the Steerable Optimizing Self-Organized Radio (SoSoRadio) system as a prototypical music rec- ommender system with robust automatic playlist gen- eration. This work describes a web-based radio system that interacts with current listeners through the selection of periodic request songs from a pool of nominees.

The useful This paper reports on the ongoing work done in the information is obviously multilevel. In this work we are GVLEX project. The aim of this multidisciplinary project not willing to design complete analysis for each level of is to design and test a storytelling humanoid robot. Ideally, interest but rather to design a multilevel analysis able to the robot would be able to process automatically a given point out the interesting parts of the tale. Based on the tale or short story, and to play it for a children audience.

In many discrimination problems a large amount of data is available but only a few of them are labeled. This provides a strong motivation to improve or develop methods for semi-supervised learning. In this paper, boosting is generalized to this task within the optimization framework of MarginBoost. We extend the margin definition to unlabeled data and develop the gradient descent algorithm that corresponds to the resulting margin cost function. This meta-learning scheme can be applied to any base classifier able to benefit from unlabeled data. We propose here to apply it to mixture models trained with an Expectation-Maximization algorithm. Promising results are presented on benchmarks with different rates of labeled data.