Abstract. We consider batch reinforcement learning problems in continuous space, expected total discounted-reward Markovian Decision Problems. As opposed to previous theoretical wo...
We present a background subtraction algorithm aimed at efciency and robustness to common sources of disturbance such as illumination changes, camera gain and exposure variations, ...
Alessandro Lanza, Federico Tombari, Luigi di Stefa...
We systematically compare five representative state-of-theart methods for estimating query language models with pseudo feedback in ad hoc information retrieval, including two var...
We devise a boosting approach to classification and regression based on column generation using a mixture of kernels. Traditional kernel methods construct models based on a single...
A wrapped feature selection process is proposed in the context of robust clustering based on Laplace mixture models. The clustering approach we consider is a generalization of the...