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Learning from Structured Data

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Encyclopedia of Machine Learning

Synonyms

Learning from complex data; Learning from non-propositional data; Learning from nonvectorial data

Definition

Learning from structured data refers to all those learning tasks where the objects to be considered as inputs and/or outputs can usefully be thought of as possessing internal structure and/or as being interrelated and dependent on each other, thus forming a structured space. Typical instances of data in structured learning tasks are sequences as they arise, e.g., in speech processing or bioinformatics, and trees or general graphs such as syntax trees in natural language processing and document analysis, molecule graphs in chemistry, relationship networks in social analysis, and link graphs in the World Wide Web. Learning from structured data presents special challenges, since the commonly used feature vector representation and/or the i.i.d. (independently and identically distributed data) assumption are no longer applicable. Different flavors of learning from structured...

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Author information

Authors and Affiliations

    Authors
    1. Tamás Horváth
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    2. Stefan Wrobel
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    Editor information

    Editors and Affiliations

    1. School of Computer Science and Engineering, University of New South Wales, Sydney, Australia, 2052

      Claude Sammut

    2. Faculty of Information Technology, Clayton School of Information Technology, Monash University, P.O. Box 63, Victoria, Australia, 3800

      Geoffrey I. Webb

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    © 2011 Springer Science+Business Media, LLC

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    Horváth, T., Wrobel, S. (2011). Learning from Structured Data. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-30164-8_458

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