software:mmr_mmmvr

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under construction

Innsbruck Maximum Margin Multi Valued Regression Framework

The source code with examples can be downloaded here.

Keywords: data inputation, matrix completion, recommender systems, low-rank approximation, kernel methods

References

Kernel-Mapping Recommender System Algorithms”, Mustansar Ghazanfar, Adam Prügel-Bennett, Sandor Szedmak, Information Sciences 208, pp. 81–104, 2012. PDF

Incremental Kernel Mapping Algorithms for Scalable Recommender Systems”, Mustansar Ghanzanfar, Sandor Szedmak, Adam Prügel-Bennett, IEEE International Conference on Tools with Artificial Intelligence, pp. 1077–1084, 2011. PDF

Knowledge Propagation and Relation Learning for Predicting Action Effects”, Sandor Szedmak, Emre Ugur, Justus Piater, IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 623–629, 2014. PDF

Learning missing edges via kernels in partially-known graphs”, Senka Krivic, Sandor Szedmak, Hanchen Xiong, Justus Piater, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning 2015. PDF

Acknowledgement

This research has received funding from the European Community’s Seventh Framework Programme FP7/2007-2013 (Speci c Programme Cooperation, Theme 3,Information and Communication Technologies) under grant agreement no. 270273, Xperience.

Contact
senka.krivic@uibk.ac.at
sandor.szedmak@aalto.fi

software/mmr_mmmvr.1458590017.txt.gz · Last modified: 2016/03/21 20:53 by Senka Krivic