Clustering Stability methods are a family of widely used model selection techniques applied in data clustering. Their unifying theme is that an appropriate model should result in ...
In this paper we propose to combine two powerful ideas, boosting and manifold learning. On the one hand, we improve ADABOOST by incorporating knowledge on the structure of the dat...
We apply learning vector quantization to the analysis of tiling microarray data. As an example we consider the classification of C. elegans genomic probes as intronic or exonic. T...
Current technologies aimed at supporting processes – whether it is a business process or a learning process – are usually based on using a dedicated set of metadata to describ...
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...