Abstract. We develop three new techniques to build on the recent advances in online learning with kernels. First, we show that an exponential speed-up in prediction time per trial ...
Abstract. This paper proposes a generic extension to propositional rule learners to handle multiple-instance data. In a multiple-instance representation, each learning example is r...
Abstract. This paper proposes a general local learning framework to effectively alleviate the complexities of classifier design by means of “divide and conquer” principle and ...
0 Temporal Abstractions and Case-Based Reasoning for Medical Course Data: Two Prognostic Applications R. Schmidt and R. Gierl University of Rostock, Germany 9.00-9.30 Local Learnin...
Abstract One of the main hurdles to improved CLIR effectiveness is resolving ambiguity associated with translation. Availability of resources is also a problem. First we present a ...