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    Automated Machine Learning

    Methods, Systems, Challenges

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    Contributor(s)
    Hutter, Frank (editor)
    Kotthoff, Lars (editor)
    Vanschoren, Joaquin (editor)
    Language
    English
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    Abstract
    This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
    URI
    http://library.oapen.org/handle/20.500.12657/23012
    Keywords
    Computer science; Artificial intelligence; Optical data processing; Pattern recognition
    DOI
    10.1007/978-3-030-05318-5
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Cham, 2019
    Series
    The Springer Series on Challenges in Machine Learning,
    Classification
    Artificial intelligence
    Pattern recognition
    Image processing
    Pages
    219
    Rights
    https://creativecommons.org/licenses/by/4.0
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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