In this paper, we propose classifier ensemble selection for Named Entity Recognition (NER) as a single objective optimization problem. Thereafter, we develop a method based on gen...
In this paper, we present a method of object classification within the context of Visual Surveillance. Our goal is the classification of tracked objects into one of the two classe...
John-Paul Renno, Dimitrios Makris, Graeme A. Jones
This paper presents a new method for constructing ensembles of classifiers based on immune network theory, one of the most interesting paradigms within the field of artificial imm...
In this paper, we present classifiers ensemble approaches for biomedical named entity recognition. Generalized Winnow, Conditional Random Fields, Support Vector Machine, and Maxim...
Ensemble methods like bagging and boosting that combine the decisions of multiple hypotheses are some of the strongest existing machine learning methods. The diversity of the memb...