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Publikationen

Zeige Ergebnisse 51 bis 60 von 540
  1. Martin Mladenov; Leonard Kleinhans; Kristian Kersting

    Lifted Convex Quadratic Programming

    In: Computing Research Repository eprint Journal (CoRR), Vol. abs/1606.04486, Pages 0-10, arXiv, 2016.

  2. Kristian Kersting; Christian Bauckhage; Mirwaes Wahabzada; Anne-Katrin Mahlein; Ulrike Steiner; Erich-Christian Oerke; Christoph Römer; Lutz Plümer

    Feeding the World with Big Data: Uncovering Spectral Characteristics and Dynamics of Stressed Plants

    In: Jörg Lässig; Kristian Kersting; Katharina Morik (Hrsg.). Computational Sustainability. Pages 99-120, Studies in Computational Intelligence, Vol. …

  3. Mayukh Das; Yuqing Wu; Tushar Khot; Kristian Kersting; Sriraam Natarajan

    Scaling Lifted Probabilistic Inference and Learning Via Graph Databases

    In: Sanjay Chawla Venkatasubramanian; Wagner Meira Jr. (Hrsg.). Proceedings of the 2016 SIAM International Conference on Data Mining. SIAM …

  4. Joseph G. Taylor; Viktoriia Sharmanska; Kristian Kersting; David Weir; Novi Quadrianto

    Learning Using Unselected Features (LUFe)

    In: Subbarao Kambhampati (Hrsg.). Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence. International Joint …

  5. Christopher Morris; Nils M. Kriege; Kristian Kersting; Petra Mutzel

    Faster Kernels for Graphs with Continuous Attributes via Hashing

    In: Francesco Bonchi; Josep Domingo-Ferrer; Ricardo Baeza-Yates; Zhi-Hua Zhou; Xindong Wu (Hrsg.). IEEE 16th International Conference on Data Mining. …

  6. Shuo Yang; Tushar Khot; Kristian Kersting; Sriraam Natarajan

    Learning Continuous-Time Bayesian Networks in Relational Domains: A Non-Parametric Approach

    In: Dale Schuurmans; Michael P. Wellman (Hrsg.). Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence. AAAI Conference on …

  7. Marion Neumann; Roman Garnett; Christian Bauckhage; Kristian Kersting

    Propagation kernels: efficient graph kernels from propagated information

    In: Machine Learning, Vol. 102, No. 2, Pages 209-245, Springer, 2016.

  8. Piotr Szymanski; Tomasz Kajdanowicz; Kristian Kersting

    How Is a Data-Driven Approach Better than Random Choice in Label Space Division for Multi-Label Classification?

    In: Entropy, Vol. 18, No. 8, Pages 0-10, MDPI, 2016.

  9. Jan Peters; Daniel D. Lee; Jens Kober; Duy Nguyen-Tuong; J. Andrew Bagnell; Stefan Schaal

    Robot Learning

    In: Bruno Siciliano; Oussama Khatib (Hrsg.). Springer Handbook of Robotics. Pages 357-398, Springer Handbooks, Springer, 2016.

  10. Christian Daniel; Gerhard Neumann; Oliver Kroemer; Jan Peters

    Hierarchical Relative Entropy Policy Search

    In: Journal of Machine Learning Research, Vol. 17, Pages 93:1-93:50, JMLR, 2016.