-
Notifications
You must be signed in to change notification settings - Fork 5
/
README
35 lines (27 loc) · 1.39 KB
/
README
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
---------------------------------------------------------------------------
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 3.6 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
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.