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Encyclopedia of Machine Learning and Data Mining pp 712–715Cite as

Learning from Structured Data

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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used feature vector representation and/or the i.i.d. (independently and identically distributed data) assumption are no longer applicable.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

  1. Fraunhofer IAIS, Schloss Birlinghoven, University of Bonn, Sankt Augustin, Germany

    Tamás Horváth

  2. Fraunhofer IAIS, Schloss Birlinghoven, University of Bonn, Sankt Augustin, Germany

    Stefan Wrobel

Authors
  1. Tamás Horváth
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  2. Stefan Wrobel
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Editor information

Editors and Affiliations

  1. The University of New South Wales, Sydney, NSW, Australia

    Claude Sammut

  2. Faculty of Information Technology, Monash University, Melbourne, VIC, Australia

    Geoffrey I. Webb

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© 2017 Springer Science+Business Media New York

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Horváth, T., Wrobel, S. (2017). Learning from Structured Data. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_458

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