An ensemble of classifiers is a set of classifiers whose predictions are combined in some way to classify new instances. Early research has shown that, in general, an ensemble of ...
The design of a CBR system involves the use of similarity metrics. For many applications, various functions can be adopted to compare case features and to aggregate them into a glo...
We propose a multiple classifier system approach to object recognition in computer vision. The aim of the approach is to use multiple experts successively to prune the list of cand...
We combine local texture features (PCA-SIFT), global features (shape context), and spatial features within a single multi-layer AdaBoost model of object class recognition. The fir...
Wei Zhang 0002, Bing Yu, Gregory J. Zelinsky, Dimi...
Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a co...