Scalable learning of graphical models

Francois Petitjean, Geoffrey I. Webb

    Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

    1 Citation (Scopus)

    Abstract

    From understanding the structure of data, to classification and topic modeling, graphical models are core tools in machine learning and data mining. They combine probability and graph theories to form a compact representation of probability distributions. In the last decade, as data stores became larger and higher-dimensional, traditional algorithms for learning graphical models from data, with their lack of scalability, became less and less usable, thus directly decreasing the potential benefits of this core technology. To scale graphical modeling techniques to the size and dimensionality of most modern data stores, data science researchers and practitioners now have to meld the most recent advances in numerous specialized fields including graph theory, statistics, pattern mining and graphical modeling. This tutorial covers the core building blocks that are necessary to build and use scalable graphical modeling technologies on large and high-dimensional data.
    Original languageEnglish
    Title of host publicationKDD'16 / KDD 2016
    Subtitle of host publicationProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA
    EditorsAlex Smola, Charu Aggarwal
    Place of PublicationNew York, NY, USA
    PublisherAssociation for Computing Machinery (ACM)
    Pages2131-2132
    Number of pages2
    ISBN (Print)9781450342322
    DOIs
    Publication statusPublished - 13 Aug 2016
    EventACM International Conference on Knowledge Discovery and Data Mining 2016 - Hilton San Francisco Union Square, San Francisco, United States of America
    Duration: 13 Aug 201617 Aug 2016
    Conference number: 22nd
    http://www.kdd.org/kdd2016/
    https://dl.acm.org/doi/proceedings/10.1145/2939672

    Conference

    ConferenceACM International Conference on Knowledge Discovery and Data Mining 2016
    Abbreviated titleKDD 2016
    Country/TerritoryUnited States of America
    CitySan Francisco
    Period13/08/1617/08/16
    OtherKDD 2016, a premier interdisciplinary conference, brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.
    Internet address

    Keywords

    • Graphical models
    • High-dimensional data
    • Structure learning

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