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, Rational Kernels: Theory and Algorithms
, Journal of Machine Learning Research
Kernels on string and sequences
- 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.
- Bernard Scholköpf and Alex Smola. Learning with kernels. MIT Press, 2002.
- 08 Oct 2007