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software:mmr_mmmvr

Innsbruck Maximum Margin Multi Valued Regression Framework

The source code with examples can be downloaded here.

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

References

  • Mustansar Ghazanfar, Adam Prügel-Bennett, Sandor Szedmak, Kernel-Mapping Recommender System Algorithms. Information Sciences 208, pp. 81–104, 2012. [DOI] © Elsevier [Link] [PDF] [Abstract] [BibTeX]

  • Mustansar Ghanzanfar, Sandor Szedmak, Adam Prügel-Bennett, Incremental Kernel Mapping Algorithms for Scalable Recommender Systems. IEEE International Conference on Tools with Artificial Intelligence, pp. 1077–1084, 2011. [DOI] © IEEE [Link] [PDF] [Abstract] [BibTeX]

  • Sandor Szedmak, Emre Ugur, Justus Piater, Knowledge Propagation and Relation Learning for Predicting Action Effects. IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 623–629, 2014. [DOI] © IEEE [Link] [PDF] [Abstract] [BibTeX]

  • Senka Krivić, Sandor Szedmak, Hanchen Xiong, Justus Piater, Learning missing edges via kernels in partially-known graphs. European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 2015. [Link] [PDF] [Abstract] [BibTeX]

    • 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 and no. 610532, Squirrel.

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

    software/mmr_mmmvr.txt · Last modified: 2018/09/03 19:35 (external edit)

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