Graph kernel: Difference between revisions

Revision as of 12:30, 2 July 2013

Revision as of 14:13, 2 July 2013

Line 13:

|url=http://jmlr.csail.mit.edu/papers/volume11/vishwanathan10a/vishwanathan10a.pdf

}}</ref>

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]], (as [['''molecule kernel]]'''s<ref name="Ralaivola2005">{{cite journal | author =L. Ralaivola, S. J. Swamidass, S. Hiroto and 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>) in [[chemoinformatics]] , and in [[social network analysis]].<ref name="Vishwanathan"/>

Graph kernels were first described in 2002 by R. I. Kondor and [[John Lafferty]]<ref>{{cite conference