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  • - A Review and Beyond
    af Krikamol Muandet
    1.098,95 kr.

    This monograph provides a comprehensive review of kernel mean embeddings of distributions and, in the course of doing so, discusses some challenging issues that could potentially lead to new research directions. The targeted audience includes graduate students and researchers in machine learning and statistics who are interested in the theory and applications of kernel mean embeddings.

  • - Part 2, Applications and Future Perspectives
    af Andrzej Cichocki
    1.078,95 kr.

    Discusses tensor network models for super-compressed higher-order representation of data/parameters and cost functions, together with an outline of their applications in machine learning and data analytics.

  • af Prateek Jain
    1.143,95 kr.

    Non-convex Optimization for Machine Learning takes an in-depth look at the basics of non-convex optimization with applications to machine learning. It introduces the rich literature in this area, as well as equips the reader with the tools and techniques needed to apply and analyze simple but powerful procedures for non-convex problems.