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Machine Learning - Design Books

You are currently browsing 1–3 of 3 new and published books in the subject of Machine Learning - Design — sorted by publish date from newer books to older books.

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New and Published Books

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  1. Computational Trust Models and Machine Learning

    Edited by Xin Liu, Anwitaman Datta, Ee-Peng Lim

    Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition

    Computational Trust Models and Machine Learning provides a detailed introduction to the concept of trust and its application in various computer science areas, including multi-agent systems, online social networks, and communication systems. Identifying trust modeling challenges that cannot be...

    Published October 28th 2014 by Chapman and Hall/CRC

  2. Regularization, Optimization, Kernels, and Support Vector Machines

    Edited by Johan A.K. Suykens, Marco Signoretto, Andreas Argyriou

    Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition

    Regularization, Optimization, Kernels, and Support Vector Machines offers a snapshot of the current state of the art of large-scale machine learning, providing a single multidisciplinary source for the latest research and advances in regularization, sparsity, compressed sensing, convex and...

    Published October 22nd 2014 by Chapman and Hall/CRC

  3. Machine Learning

    An Algorithmic Perspective, Second Edition

    By Stephen Marsland

    Series: Chapman & Hall/CRC Machine Learning & Pattern Recognition

    A Proven, Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning...

    Published October 7th 2014 by Chapman and Hall/CRC

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