---------------------------------------------------------------------------------------------------- PAC-BAYESIAN DOMAIN ADAPTATION (aka PBDA) Version 0.901 (August 9, 2013), Released under the BSD-license http://graal.ift.ulaval.ca/pbda/ ---------------------------------------------------------------------------------------------------- Author: Pascal Germain. Groupe de Recherche en Apprentissage Automatique de l'Universite Laval (GRAAL). Reference: Pascal Germain, Amaury Habrard, Francois Laviolette, and Emilie Morvant. A PAC-Bayesian Approach for Domain Adaptation with Specialization to Linear Classifiers. International Conference on Machine Learning (ICML) 2013. ---------------------------------------------------------------------------------------------------- Thank you for looking at my code! This program have been tested using Python 2.7.4 under Linux. It requires the NumPy and SciPy libraries. I prepared three small scripts to use PBDA by the command line: 1) pbda_learn.py: Execute the learning algorithm 2) pbda_classify.py: Execute the classification function 3) pbda_reverse_cv.py: Compute a "reverse cross-validation" score Further usage instructions can be obtained by the following commands: python pbda_learn.py --help python pbda_classify.py --help python pbda_reverse_cv.py --help For more informations, please visit: http://graal.ift.ulaval.ca/pbda/ Pascal Germain.