Abstract. We establish a generic theoretical tool to construct probabilistic bounds for algorithms where the output is a subset of objects from an initial pool of candidates (or mo...
When envisaging new digital instruments, designers do not have to limit themselves to their sonic capabilities (which can be absolutely any), not even to their algorithmic power; ...
When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit observations, as they are read from a database, we call the process stru...
In transfer learning the aim is to solve new learning tasks using fewer examples by using information gained from solving related tasks. Existing transfer learning methods have be...
An adaptive semi-supervised ensemble method, ASSEMBLE, is proposed that constructs classification ensembles based on both labeled and unlabeled data. ASSEMBLE alternates between a...