We study the problem of learning using combinations of machines. In particular we present new theoretical bounds on the generalization performance of voting ensembles of kernel ma...
We introduce an expandable Bayesian network (EBN) to handle the combination of diverse multiple homogeneous evidence sets. An EBN is an augmented Bayesian network which instantiat...
Most machine learning algorithms share the following drawback: they only output bare predictions but not the con dence in those predictions. In the 1960s algorithmic information t...
A way of combining object-oriented and structural paradigms of software composition is demonstrated in a tool for generative programming. Metaclasses are introduced that are compon...
We present a resolution-based decision procedure for the description logic SHOIQ--the logic underlying the Semantic Web ontology language OWL-DL. Our procedure is goal-oriented, an...