Margin-maximizing techniques such as boosting have been generating excitement in machine learning circles for several years now. Although these techniques offer significant impro...
Supporting continuous mining queries on data streams requires algorithms that (i) are fast, (ii) make light demands on memory resources, and (iii) are easily to adapt to concept dr...
Abstract. One of the potential advantages of multiple classifier systems is an increased robustness to noise and other imperfections in data. Previous experiments on classificati...
Boosting constructs a weighted classifier out of possibly weak learners by successively concentrating on those patterns harder to classify. While giving excellent results in many ...
Boosting methods are known to exhibit noticeable overfitting on some datasets, while being immune to overfitting on other ones. In this paper we show that standard boosting algorit...
We introduce a new class of image features, the Ray feature set, that consider image characteristics
at distant contour points, capturing information which is difficult to repre...