摘要: Graphs are a major tool for modeling objects with complex data structures. Devising learning algorithms that able to handle graph representations is thus core issue in knowledge discovery data. While significant amount of recent research has been devoted inducing functions on the vertices graph, we concentrate task function set graphs. Application areas such range from computer vision biology and beyond. Here, present number results extending kernel methods data, general, representations, particular. With very good performance can easily be embedded Euclidean space, have potential overcome some weaknesses previous approaches In order apply propose two different compare them relational reinforcement problem molecule classification problem.
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