@InProceedings{Supelec334,
author = {Virginie Galtier and Olivier Pietquin and Stephane Vialle},
title = {{AdaBoost Parallelization on PC Clusters with Virtual Shared Memory for Fast Feature Selection}},
year = {2007},
booktitle = {{Proceedings of the 1st IEEE International Conference on Signal Processing and Communication}},
pages = {165-168},
month = {November},
address = {Dubai (United Arab Emirates)},
url = {http://hal-supelec.archives-ouvertes.fr/hal-00216041/fr/},
abstract = {Feature selection is a key issue in many machine learning
applications and the need to test lots of candidate features is
real while computational time required to do so is often huge.
In this paper, we introduce a parallel version of the well-
known AdaBoost algorithm to speed up and size up feature
selection for binary classification tasks using large training
datasets and a wide range of elementary features. This
parallelization is done without any modification to the
AdaBoost algorithm and designed for PC clusters using Java and
the JavaSpace distributed framework. JavaSpace is a memory
sharing paradigm implemented on top of a virtual shared memory,
that appears both efficient and easy-to-use. Results and
performances on a face detection system trained with the
proposed parallel AdaBoost are presented. }
}