We present an improved bound on the difference between training and test errors for voting classifiers. This improved averaging bound provides a theoretical justification for popu...
This paper presents a learning theoretical analysis of correlation clustering (Bansal et al., 2002). In particular, we give bounds on the error with which correlation clustering r...
In this paper, we examine the advantages and disadvantages of filter and wrapper methods for feature selection and propose a new hybrid algorithm that uses boosting and incorporat...