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Erschienen in:
2006 | OriginalPaper | Buchkapitel
Graph Machines and Their Applications to Computer-Aided Drug Design: A New Approach to Learning from Structured Data
verfasst von : Aurélie Goulon, Arthur Duprat, Gérard Dreyfus
Erschienen in: Unconventional Computation
Verlag: Springer Berlin Heidelberg
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The recent developments of statistical learning focused on
vector machines,
which learn from examples that are described by vectors of features. However, there are many fields where structured data must be handled; therefore, it would be desirable to learn from examples described by
graphs.Graph machines
learn real numbers from graphs. Basically, for each graph, a separate learning machine is built, whose algebraic structure contains the same information as the graph. We describe the training of such machines, and show that virtual leave-one-out, a powerful method for assessing the generalization capabilities of conventional vector machines, can be extended to graph machines. Academic examples are described, together with applications to the prediction of pharmaceutical activities of molecules and to the classification of properties; the potential of graph machines for computer-aided drug design are highlighted.