We propose a new framework for supervised machine learning. Our goal is to learn from smaller amounts of supervised training data, by collecting a richer kind of training data: an...
This paper presents a method that assists in maintaining a rule-based named-entity recognition and classification system. The underlying idea is to use a separate system, construc...
Georgios Petasis, Frantz Vichot, Francis Wolinski,...
The performance of video analysis and indexing algorithms strongly depends on the type, content and recording characteristics of the analyzed video. Current video indexing approac...
This paper promotes the use of supervised machine learning in laboratory settings where chemists have a large number of samples to test for some property, and are interested in id...
Abstract. In this paper, we propose and study a new on-line algorithm for learning a SVM based on Radial Basis Function Kernel: Local Incremental Learning of SVM or LISVM. Our meth...