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Learning algorithm described in "A New PAC-Bayesian Perspective on Domain Adaptation" (see http://arxiv.org/abs/1506.04573)
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Sabrout/domain_adaptation_of_linear_classifiers
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--------------------------------------------------------------------------- DOMAIN ADAPTATION OF LINEAR CLASSIFIERS (aka DALC) Version 0.90 (November 2, 2015), Released under the BSD-license --------------------------------------------------------------------------- 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 New PAC-Bayesian Perspective on Domain Adaptation. International Conference on Machine Learning (ICML) 2016. http://arxiv.org/abs/1506.04573 ---------------------------------------------------------------------------- Thank you for looking at my code! This program have been tested using Python 3.5 under Linux and MacOS. It requires the NumPy and SciPy libraries. I prepared three small scripts to use DALC by the command line: 1) dalc_learn.py: Execute the learning algorithm 2) dalc_classify.py: Execute the classification function 3) dalc_reverse_cv.py: Compute a "reverse cross-validation" score Further usage instructions can be obtained by the following commands: python dalc_learn.py --help python dalc_classify.py --help python dalc_reverse_cv.py --help Pascal Germain.
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Learning algorithm described in "A New PAC-Bayesian Perspective on Domain Adaptation" (see http://arxiv.org/abs/1506.04573)
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