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    G-Maximization: an Unsupervised Learning Procedure for Discovering Regularities

    Pearlmutter, Barak A. and Hinton, Geoffrey (1986) G-Maximization: an Unsupervised Learning Procedure for Discovering Regularities. AIP Conference Proceedings, 151. pp. 333-338. ISSN 0094-243X

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    Hill climbing is used to maximize an information theoretic measure of the difference between the actual behavior of a unit and the behavior that would be predicted by a statistician who knew the first order statistics of the inputs but believed them to be independent. This causes the unit to detect higher order correlations among its inputs. Initial simulations are presented, and seem encouraging. We describe an extension of the basic idea which makes it resemble competitive learning and which causes members of a population of these units to differentiate, each extracting different structure from the input.

    Item Type: Article
    Keywords: G-Maximization; Unsupervised Learning Procedure; Discovering Regularities;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 5533
    Depositing User: Barak Pearlmutter
    Date Deposited: 03 Nov 2014 16:07
    Journal or Publication Title: AIP Conference Proceedings
    Publisher: American Institute of Physics
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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