Supervised graph inference

Jean Philippe Vert, Yoshihiro Yamanishi

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    47 Citations (Scopus)

    Abstract

    We formulate the problem of graph inference where part of the graph is known as a supervised learning problem, and propose an algorithm to solve it. The method involves the learning of a mapping of the vertices to a Euclidean space where the graph is easy to infer, and can be formulated as an optimization problem in a reproducing kernel Hilbert space. We report encouraging results on the problem of metabolic network reconstruction from genomic data.

    Original languageEnglish
    Title of host publicationAdvances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004
    PublisherNeural information processing systems foundation
    ISBN (Print)0262195348, 9780262195348
    Publication statusPublished - 2005
    Event18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada
    Duration: Dec 13 2004Dec 16 2004

    Publication series

    NameAdvances in Neural Information Processing Systems
    ISSN (Print)1049-5258

    Other

    Other18th Annual Conference on Neural Information Processing Systems, NIPS 2004
    CountryCanada
    CityVancouver, BC
    Period12/13/0412/16/04

    All Science Journal Classification (ASJC) codes

    • Computer Networks and Communications
    • Information Systems
    • Signal Processing

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