Probabilistic feature relevance learning (PFRL) is an effective method for adaptively computing local feature relevance in content-based image retrieval. It computes flexible retr...
The past few years have seen a surge of interest in the field of probabilistic logic learning and statistical relational learning. In this endeavor, many probabilistic logics have...
Angelika Kimmig, Bart Demoen, Luc De Raedt, V&iacu...
We introduce the posterior probabilistic clustering (PPC), which provides a rigorous posterior probability interpretation for Nonnegative Matrix Factorization (NMF) and removes th...
We analyse the kinematics of probabilistic term weights at retrieval time for di erent Information Retrieval models. We present four models based on di erent notions of probabilis...
—Various sensor types, e.g., temperature, humidity, and acoustic, sense physical phenomena in different ways, and thus, are expected to have different sensing models. Even for th...