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Efficient Geometric-based Computation of the String Subsequence Kernel
[article]
2015
arXiv
pre-print
Kernel methods are powerful tools in machine learning. They have to be computationally efficient. In this paper, we present a novel Geometric-based approach to compute efficiently the string subsequence kernel (SSK). Our main idea is that the SSK computation reduces to range query problem. We started by the construction of a match list L(s,t)={(i,j):s_i=t_j} where s and t are the strings to be compared; such match list contains only the required data that contribute to the result. To compute
arXiv:1502.07776v1
fatcat:pg5yndbj6zfszdo64mgdylco4a