Symbolic Learning vs. Graph Kernels: An Experimental Comparison in a Chemical Application
نویسندگان
چکیده
In this paper we present a quantitative comparison between two approaches, Graph Kernels and Symbolic Learning, within a classification scheme. The experimental case-study is the predictive toxicology evaluation, that is the inference of the toxic characteristics of chemical compounds from their structure. The results demonstrate that both approaches are comparable in terms of accuracy, but present pros and cons that are discussed in the last part of the paper.
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