PAC-Bayesian Domain Adaptation (aka PBDA) -- machine learning algorithm
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LICENSE
README
__init__.py
common.py
dataset.py
kernel.py
pbda.py
pbda_classify.py
pbda_learn.py
pbda_reverse_cv.py

README

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PAC-BAYESIAN DOMAIN ADAPTATION (aka PBDA)
Version 0.901 (August 9, 2013), Released under the BSD-license
http://graal.ift.ulaval.ca/pbda/ 
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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.