In this paper we propose a new distributed learning method called distributed network boosting (DNB) algorithm for distributed applications. The learned hypotheses are exchanged b...
In this paper we focus on the adaptation of boosting to grammatical inference. We aim at improving the performances of state merging algorithms in the presence of noisy data by us...
Jean-Christophe Janodet, Richard Nock, Marc Sebban...
Abstract. Computation time is an important performance characteristic of computer vision algorithms. This paper shows how existing (slow) binary-valued decision algorithms can be a...
Abstract. We focus on two recently proposed algorithms in the family of “boosting”-based learners for automated text classification, AdaBoost.MH and AdaBoost.MHKR . While the ...
Pio Nardiello, Fabrizio Sebastiani, Alessandro Spe...
This work focuses on algorithms which learn from examples to perform multiclass text and speech categorization tasks. Our approach is based on a new and improved family of boosting...