Abstract
We introduce several new families of string kernels designed in particular for use with support vector machines (SVMs) for classification of protein sequence data. These kernels – restricted gappy kernels, substitution kernels, and wildcard kernels – are based on feature spaces indexed by k-length subsequences from the string alphabet Σ (or the alphabet augmented by a wildcard character), and hence they are related to the recently presented (k,m)-mismatch kernel and string kernels used in text classification. However, for all kernels we define here, the kernel value K(x,y) can be computed in O(c K (|x| + |y|)) time, where the constant c K depends on the parameters of the kernel but is independent of the size |Σ| of the alphabet. Thus the computation of these kernels is linear in the length of the sequences, like the mismatch kernel, but we improve upon the parameter-dependent constant \(c_K = k^{m+1} |\Sigma|^m\) of the mismatch kernel. We compute the kernels efficiently using a recursive function based on a trie data structure and relate our new kernels to the recently described transducer formalism. Finally, we report protein classification experiments on a benchmark SCOP dataset, where we show that our new faster kernels achieve SVM classification performance comparable to the mismatch kernel and the Fisher kernel derived from profile hidden Markov models.
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Leslie, C., Kuang, R. (2003). Fast Kernels for Inexact String Matching. In: Schölkopf, B., Warmuth, M.K. (eds) Learning Theory and Kernel Machines. Lecture Notes in Computer Science(), vol 2777. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45167-9_10
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DOI: https://doi.org/10.1007/978-3-540-45167-9_10
Publisher Name: Springer, Berlin, Heidelberg
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