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CyrilAllauzen
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---+ !OpenKernel Background Material Kernel methods (such as SVM, perceptron, kernel PCA) are widely used in machine learning. Applying these methods in text and language processing or bioinformatics requires to be able to define kernels on sequences or distributions of sequences. _Rational kernels_ are a general framework for defining such kernels. Commonly used kernels on sequences such as n-gram kernels, gappy n-gram kernels, mismatch kernels, and Hausler's convolution kernels are special cases of rational kernels. The notion of _rational kernel_ was introduced by: Corinna Cortes, Patrick Haffner and Mehryar Mohri, [[http://www.cs.nyu.edu/~mohri/postscript/jmlr.pdf][Rational Kernels: Theory and Algorithms%ICON{download}%]], _Journal of Machine Learning Research_ 5:1035--1062, 2004. ---+++ References __Rational kernels__ * Corinna Cortes, Patrick Haffner and Mehryar Mohri, [[http://www.cs.nyu.edu/~mohri/postscript/jmlr.pdf][Rational Kernels: Theory and Algorithms%ICON{download}%]], _Journal of Machine Learning Research_ 5:1035--1062, 2004. __Kernels on string and sequences__ * Huma Lodhi, Craig Saunders, John Shawe-Taylor, Nello Cristianini and Chris Watskins. [[http://www.jmlr.org/papers/volume2/lodhi02a/lodhi02a.pdf][Text Classification using String Kernels%ICON{download}%]], _Journal of Machine Learning Research_ 2:419-444, 2002. * Christina S. Leslie, Eleazar Eskin, Adiel Cohen, Jason Weston and William Stafford Noble. Mismatch string kernels for discriminative protein classification. _Bioinformatics_ 20(4):467-476, 2004. * David Hausler. Convolution kernels on discrete structures. Technical Report UCSC-CRL-99-10, University of California at Santa Cruz, 1999. __Kernel methods__ * Bernard Scholköpf and Alex Smola. _Learning with kernels_. MIT Press, 2002. -- Main.CyrilAllauzen - 08 Oct 2007
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Topic revision: r6 - 2009-02-04
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CyrilAllauzen
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