Efficient classification learning of biochemical structured data by means of relevance weighting for sensoric response features Konferenzpaper uri icon



  • We present an approach for generating vectorial represen-
    tations of graphs for machine learning applications based on a sensoric
    response principle and multiple graph kernels. The sensor perspective re-
    duces the graph kernel computations significantly. Thus, multiple kernel
    (relevance) learning can be realized using the interpretable generalized ma-
    trix learning vector quantization (GMLVQ) classifier. Results obtained in
    small molecule classification serve as proof of concept.


  • 2022


  • Open Access


  • 445

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