Affiliations: Université de Lyon, F-69003, Lyon, France
Université de Saint-Etienne, F-42000, St-Etienne, France. UMR-CNRS
5516, Laboratoire Hubert Curien 18 rue du Professeur Benoit Lauras, F-42000,
St-Etienne, France. E-mail: {janodet,Marc.Sebban}@univ-st-etienne.fr | Artefacto, 11 rue Meynier, F-35700 Rennes, France.
E-mail: [email protected]
Abstract: We focus on the adaptation of boosting to representation spaces
composed of different subsets of features. Rather than imposing a single weak
learner to handle data that could come from different sources (e.g., images and
texts and sounds), we suggest the decomposition of the learning task into
several dependent sub-problems of boosting, treated by different weak learners,
that will optimally collaborate during the weight update stage. To achieve this
task, we introduce a new weighting scheme for which we provide theoretical
results. Experiments are carried out and show that ourmethod works
significantly better than any combination of independent boosting procedures.
