PAC-Bayesian Domain Adaptation (aka PBDA) -- machine learning algorithm
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---------------------------------------------------------------------------------------------------- PAC-BAYESIAN DOMAIN ADAPTATION (aka PBDA) Version 0.901 (August 9, 2013), Released under the BSD-license https://github.com/pgermain/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 3.6 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 The data used in the paper experiments is available here (in svmlight format): http://researchers.lille.inria.fr/pgermain/data/amazon_tfidf_svmlight.tgz Pascal Germain.
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