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751

9th SaLTMiL Workshop on “Free/open-Source Language Resources for the Machine Translation of Less-Resourced Languages” LREC 2014, Reykjavík, Iceland, 27 MayWorkshop Programme

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Source URL: ixa2.si.ehu.es

- Date: 2014-04-16 07:15:05
    752

    Journal of Machine Learning Research1210 Submitted 12/12; Revised 8/14; Published 6/15 Learning the Structure and Parameters of Large-Population Graphical Games from Behavioral Data

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    Source URL: jmlr.org

    - Date: 2015-07-27 17:41:58
      753

      974 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 31,

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      Source URL: www.cad.zju.edu.cn

      - Date: 2014-05-21 23:30:30
        754

        394 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 29,

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        Source URL: visal.cs.cityu.edu.hk

        - Date: 2010-08-17 10:24:11
          755

          R.W. Hartenstein, A.G. Hirschbiel, M. Riedmuller, K. Schmidt, M.Weber: A High Performance Machine Paradigm Based on Auto-Sequencing Data Memory; HICSS-24, Hawaii Int. Conference on System Sciences, Koloa Hawaii, 1991 A

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          Source URL: www.fpl.uni-kl.de

          - Date: 2016-05-16 11:51:51
            756

            EU-BRIDGE MT: Combined Machine Translation ∗ Markus Freitag, ∗ Stephan Peitz, ∗ Joern Wuebker, ∗ Hermann Ney, Huck, ‡ Rico Sennrich, ‡ Nadir Durrani, ‡ Maria Nadejde, ‡ Philip Williams, ‡ Philipp Koehn,

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            Source URL: www.aclweb.org

            - Date: 2014-06-16 05:56:23
              757

              The Machine Learning Database Datacratic - Montr´eal, Canada - Abstract—The Machine Learning Database1 (MLDB) is a novel open source system for solving big data machine learning problems, from data

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              Source URL: blog.mldb.ai

              - Date: 2016-12-20 15:15:45
                758

                Journal of Machine Learning Research () Submitted ; Published Why is Posterior Sampling Better than Optimism for Reinforcement Learning?

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                Source URL: ewrl.files.wordpress.com

                - Date: 2016-11-23 05:12:31
                  759

                  Unsupervised and Supervised Machine Learning in User Modeling for Intelligent Learning Environments Saleema Amershi1 and Cristina Conati1,2 2 Dept. of Computer Science, Dept. of Information and Communication Technology

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                  Source URL: www.cs.ubc.ca

                  - Date: 2007-01-24 15:02:52
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