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Conference Paper

Mismatch String Kernels for SVM Protein Classification

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons84311

Eskin E, Weston,  J
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Leslie, C., Eskin E, Weston, J., & Noble, W. (2003). Mismatch String Kernels for SVM Protein Classification. Advances in Neural Information Processing Systems, 1417-1424.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-DB47-C
Abstract
We introduce a class of string kernels, called mismatch kernels, for use with support vector machines (SVMs) in a discriminative approach to the protein classification problem. These kernels measure sequence similarity based on shared occurrences of k-length subsequences, counted with up to m mismatches, and do not rely on any generative model for the positive training sequences. We compute the kernels efficiently using a mismatch tree data structure and report experiments on a benchmark SCOP dataset, where we show that the mismatch kernel used with an SVM classifier performs as well as the Fisher kernel, the most successful method for remote homology detection, while achieving considerable computational savings.