Metadata-Version: 1.1
Name: libtlda
Version: 0.1.1
Summary: Library of transfer learning and domain adaptation                   classifiers.
Home-page: https://github.com/wmkouw/libTLDA
Author: Wouter Kouw
Author-email: wmkouw@gmail.com
License: Apache 2.0
Project-URL: Tracker, https://github.com/wmkouw/libTLDA/issues
Description: [![BuildStatus](https://travis-ci.org/wmkouw/libTLDA.svg?branch=master)](https://travis-ci.org/wmkouw/libTLDA)
        ## libTLDA: library of transfer learning and domain adaptation classifiers.
        
        This package contains the following classifiers:
        - Importance-weighted classifier, with weight estimators:<br>
        	- Kernel density estimation <br>
        	- Ratio of Gaussians [(Shimodaira, 2000)](https://www.sciencedirect.com/science/article/pii/S0378375800001154) <br>
        	- Logistic discrimination [(Bickel et al., 2009)](http://www.jmlr.org/papers/v10/bickel09a.html) <br>
        	- Kernel Mean Matching [(Huang et al., 2006)](https://papers.nips.cc/paper/3075-correcting-sample-selection-bias-by-unlabeled-data) <br>
        	- Nearest-neighbour-based weighting [(Loog, 2015)](http://ieeexplore.ieee.org/document/6349714/) <br>
        - Transfer Component Analysis [(Pan et al, 2009)](http://ieeexplore.ieee.org/document/5640675/) <br>
        - Subspace Alignment [(Fernando et al., 2013)](https://dl.acm.org/citation.cfm?id=1610094) <br>
        - Structural Correspondence Learning [(Blitzer et al., 2006)](https://dl.acm.org/citation.cfm?id=1610094) <br>
        - Robust Bias-Aware [(Liu & Ziebart, 2014)](https://papers.nips.cc/paper/5458-robust-classification-under-sample-selection-bias) <br>
        - Feature-Level Domain Adaptation [(Kouw et al., 2016)](http://jmlr.org/papers/v17/15-206.html) <br>
        
        ## Python
        Python versions 2.7, 3.4, 3.5 and 3.6.
        
        ### Installation
        First clone and enter the repository:
        ```
        sudo apt-get install git
        git clone https://github.com/wmkouw/libTLDA
        cd libTLDA/
        ```
        
        Conda environments take care of all dependencies. If you don't have conda installed already, you can set it up through:
        ```
        wget http://repo.continuum.io/miniconda/Miniconda2-latest-$(uname)-x86_64.sh -O ~/miniconda.sh
        bash ~/miniconda.sh -b -p $HOME/miniconda
        export PATH="$HOME/miniconda/bin:$PATH"
        ```
        Then, create and activate a new environment:
        ```
        conda env create -f environment.yml
        source activate libtlda
        ```
        
        Afterwards, the package can be installed by running the following setup script:
        ```
        python setup.py install
        ```
        
        ### Usage
        The script in `example.py` shows a simple example of importing one of the adaptive classifiers and applying them to your data set.
        ```
        cd python/
        python example.py
        ```
        
        <!-- ### Python-specific classifiers
        - dann: Domain-Adversarial Neural Network (Ganin et al., 2015) (TODO) -->
        
        ## Matlab
        Version: 9.2.0.556344 (R2017a) <br>
        
        ### Installation:
        First clone the repository and change directory to matlab:
        ```
        sudo apt-get install git
        git clone https://github.com/wmkouw/libTLDA
        cd libTLDA/matlab/
        ```
        
        In the matlab command window, call the installation script. It downloads all dependencies ([minFunc](https://www.cs.ubc.ca/~schmidtm/Software/minFunc.html), [libsvm](https://www.csie.ntu.edu.tw/~cjlin/libsvm/)) and adds them - along with libTLDA - to your path:
        ```
        install.m
        ```
        
        ### Usage
        There is an example script that can be edited to test the different classifiers:
        ```
        example.m
        ```
        
        ### Matlab-specific classifiers:
        - Geodesic Flow Kernel [(Gong et al., 2012)](https://dl.acm.org/citation.cfm?id=1610094) <br>
        
        ## Contact:
        Questions, comments and bugs can be submitted in the [issues tracker](https://github.com/wmkouw/libTLDA/issues).
        
Keywords: machine learning somain adaptation transfer learning
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: POSIX :: Linux
Classifier: Operating System :: MacOS
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Software Development :: Libraries
