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not all molecule kernels are graph kernels |
Link to John Lafferty article was wrong. Removed. |
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Graph kernels can be intuitively understood as functions measuring the similarity of pairs of graphs. They allow [[Kernel trick|kernelized]] learning algorithms such as [[support vector machine]]s to work directly on graphs, without having to do [[feature extraction]] to transform them to fixed-length, real-valued [[feature vector]]s. They find applications in [[bioinformatics]], in [[chemoinformatics]] (as a type of [[molecule kernel]]s<ref name="Ralaivola2005">{{cite journal |author1=L. Ralaivola |author2=S. J. Swamidass |author3=S. Hiroto |author4=P. Baldi |title=Graph kernels for chemical informatics |journal=Neural Networks |year=2005 |volume=18 |pages=1093–1110 |url=http://www.sciencedirect.com/science/article/pii/S0893608005001693}}</ref>), and in [[social network analysis]].<ref name="Vishwanathan"/>
Graph kernels were first described in 2002 by R. I. Kondor and
|title=Diffusion Kernels on Graphs and Other Discrete Input Spaces
|author1=Risi Imre Kondor
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