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Learning from structured data by finger printing
KTH, School of Information and Communication Technology (ICT), Computer and Systems Sciences, DSV.
KTH, School of Information and Communication Technology (ICT), Computer and Systems Sciences, DSV.
2006 (English)In: Publications of the Finnish Artificial Intelligence Society, 2006, 120-126 p.Conference paper, Published paper (Refereed)
Abstract [en]

Current methods for learning from structured data are limited w.r.t. handling large or isolated substructures and also impose constraints on search depth and induced structure length. An approach to learning from structured data using a graph based canonical representation method of structures, called finger printing, is introduced that addresses the limitations of current methods. The method is implemented in a system, called DIFFER, which is demonstrated to compare favourable to existing state-of-art methods on some benchmark data sets. It is shown that further improvements can be obtained by combining the features generated by finger printing with features generated by previous methods.

Place, publisher, year, edition, pages
2006. 120-126 p.
Series
Publications of the Finnish Artificial Intelligence Society, ISSN 1796-623X
Keyword [en]
Benchmark data, Canonical representations, Finger printing, Graph-based, Induced structures, State-of-art methods, Structured data, Artificial intelligence, Printing
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-155040Scopus ID: 2-s2.0-84862568162ISBN: 9525677001 (print)ISBN: 9789525677003 (print)OAI: oai:DiVA.org:kth-155040DiVA: diva2:759446
Conference
9th Scandinavian Conference on Artificial Intelligence, SCAI 2006, 25 October 2006 through 27 October 2006, Espoo
Note

QC 20141030

Available from: 2014-10-30 Created: 2014-10-29 Last updated: 2016-12-08Bibliographically approved

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Citation style
  • apa
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  • nn-NB
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  • Other locale
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Output format
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