Logistic regression for graph classification
نویسنده
چکیده
In this paper we deal with graph classification. We propose a new algorithm for performing sparse logistic regression on graphs. Sparsity is required for the reason of interpretability, which is often necessary in domains such as bioinformatics or chemoinformatics. Our method is comparable in accuracy with other methods of graph classification and produces probabilistic output in addition.
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تاریخ انتشار 2009