scikit-multimodallearn 0.1.0

Documentation

The documentation including installation instructions, API documentation and examples is available online.

Installation

pip install scikit-multimodallearn

Development

The development of this package follows the guidelines provided by the scikit-learn community.

Refer to the Developer’s Guide of the scikit-learn project for more details.

git clone git@gitlab.lis-lab.fr:dev/scikit-multimodallearn.git

git clone git@github.com:multi-learn/scikit-multimodallearn.git

cd multimodal
pytest

python setup.py build_sphinx

Credits

scikit-multimodallearn is developped by the development team of the LIS.

If you use scikit-multimodallearn in a scientific publication, please cite the following paper:

@InProceedings{Koco:2011:BAMCC,
 author={Ko\c{c}o, Sokol and Capponi, C{\'e}cile},
 editor={Gunopulos, Dimitrios and Hofmann, Thomas and Malerba, Donato
         and Vazirgiannis, Michalis},
 title={A Boosting Approach to Multiview Classification with Cooperation},
 booktitle={Proceedings of the 2011 European Conference on Machine Learning
            and Knowledge Discovery in Databases - Volume Part II},
 year={2011},
 location={Athens, Greece},
 publisher={Springer-Verlag},
 address={Berlin, Heidelberg},
 pages={209--228},
 numpages = {20},
 isbn={978-3-642-23783-6}
 url={https://link.springer.com/chapter/10.1007/978-3-642-23783-6_14},
 keywords={boosting, classification, multiview learning,
           supervised learning},
}

@InProceedings{Huu:2019:BAMCC,
 author={Huusari, Riika, Kadri Hachem and Capponi, C{\'e}cile},
 editor={},
 title={Multi-view Metric Learning in Vector-valued Kernel Spaces},
 booktitle={arXiv:1803.07821v1},
 year={2018},
 location={Athens, Greece},
 publisher={},
 address={},
 pages={209--228},
 numpages = {12}
 isbn={978-3-642-23783-6}
 url={https://link.springer.com/chapter/10.1007/978-3-642-23783-6_14},
 keywords={boosting, classification, multiview learning,
           merric learning, vector-valued, kernel spaces},
}