Kernels for structured data

Thomas Gärtner, John W. Lloyd, Peter A. Flach

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

    18 Citations (Scopus)

    Abstract

    Learning from structured data is becoming increasingly important. However, most prior work on kernel methods has focused on learning from attribute-value data. Only recently have researchers started investigating kernels for structured data. This paper describes how kernel definitions can be simplified by identifying the structure of the data and how kernels can be defined on this structure. We propose a kernel for structured data, prove that it is positive definite, and show how it can be adapted in practical applications.

    Original languageEnglish
    Title of host publicationInductive Logic Programming
    EditorsStan Matwin, Claude Sammut
    PublisherSpringer Verlag
    Pages66-83
    Number of pages18
    ISBN (Electronic)9783540005674
    DOIs
    Publication statusPublished - 2003
    Event12th International Conference on Inductive Logic Programming, ILP 2002 - Sydney, Australia
    Duration: 9 Jul 200211 Jul 2002

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume2583
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference12th International Conference on Inductive Logic Programming, ILP 2002
    Country/TerritoryAustralia
    CitySydney
    Period9/07/0211/07/02

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