Udvidet returret til d. 31. januar 2025

Bøger af Eyke Hüllermeier

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  • af Eyke Hüllermeier, Thomas Fober, Sebastian Link & mfl.
    602,95 kr.

  • af Eyke Hüllermeier, Rosa Meo, Toon Calders & mfl.
    594,95 - 609,95 kr.

  • af Eyke Hüllermeier, Rosa Meo, Toon Calders & mfl.
    610,95 kr.

    This three-volume set LNAI 8724, 8725 and 8726 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: ECML PKDD 2014, held in Nancy, France, in September 2014. The 115 revised research papers presented together with 13 demo track papers, 10 nectar track papers, 8 PhD track papers, and 9 invited talks were carefully reviewed and selected from 550 submissions. The papers cover the latest high-quality interdisciplinary research results in all areas related to machine learning and knowledge discovery in databases.

  • - 16th International Conference, DS 2013, Singapore, October 6-9, 2013, Proceedings
    af Johannes Fürnkranz
    577,95 kr.

    This book constitutes the proceedings of the 16th International Conference on Discovery Science, DS 2013, held in Singapore in October 2013, and co-located with the International Conference on Algorithmic Learning Theory, ALT 2013. The 23 papers presented in this volume were carefully reviewed and selected from 52 submissions. They cover recent advances in the development and analysis of methods of automatic scientific knowledge discovery, machine learning, intelligent data analysis, and their application to knowledge discovery.

  • af Eyke Hüllermeier
    1.686,95 kr.

    Making use of different frameworks of approximate reasoning and reasoning under uncertainty, notably probabilistic and fuzzy set-based techniques, this book develops formal models of the above inference principle, which is fundamental to CBR.

  • af Eyke Hüllermeier
    1.745,95 kr.

    Making use of different frameworks of approximate reasoning and reasoning under uncertainty, notably probabilistic and fuzzy set-based techniques, this book develops formal models of the above inference principle, which is fundamental to CBR.

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