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  • Maximum Common Subgraph based locally weighted regression

    Seeland, Madeleine; Buchwald, Fabian; Kramer, Stefan; Pfahringer, Bernhard (ACM, 2012)
    This paper investigates a simple, yet effective method for regression on graphs, in particular for applications in chem-informatics and for quantitative structure-activity relationships (QSARs). The method combines Locally ...
  • Model selection based product kernel learning for regression on graphs

    Seeland, Madeleine; Kramer, Stefan; Pfahringer, Bernhard (ACM, 2013)
    The choice of a suitable graph kernel is intrinsically hard and often cannot be made in an informed manner for a given dataset. Methods for multiple kernel learning offer a possible remedy, as they combine and weight kernels ...

Madeleine Seeland has 3 co-authors in Research Commons.