Abstract. Improving accuracy in Information Retrieval tasks via semantic information is a complex problem characterized by three main aspects: the document representation model, th...
Roberto Basili, Marco Cammisa, Alessandro Moschitt...
This paper describes a novel application of Statistical Learning Theory (SLT) for motion prediction. SLT provides analytical VC-generalization bounds for model selection; these bo...
Harry Wechsler, Zoran Duric, Fayin Li, Vladimir Ch...
The meta-learner MLR (Multi-response Linear Regression) has been proposed as a trainable combiner for fusing heterogeneous baselevel classifiers. Although it has interesting prope...
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 ...
A new method for tracking contours of moving objects in clutter is presented. For a given object, a model of its contours is learned from training data in the form of a subset of ...