D08-1042 the leaf projection paths using string kernels . A few kernels based on dependency
D09-1029 the generic definition of the string kernel in order to take into account
D09-1142 representation generated by the string kernel is used to find a hyperplane
D10-1023 similarity between two examples . The string kernel we described in the previous
D09-1142 the substrings generated by the string kernel have no position information
D10-1023 al. , 2002 ) . Informally , a string kernel aims to efficiently compute the
C04-1109 quite well for the task . Although string kernels can capture common word subsequences
D10-1023 it uses a Regression SVM with a string kernel to predict essay scores . 6.3
D09-1029 feature space associated with the string kernel of length n is indexed by a set
D09-1029 syntagmatic kernels as a combination of string kernels applied to sequences of words
D09-1029 sequences of words . Therefore , the string kernel ( Shawe-Taylor and Cristianini
D08-1082 built on top of SVMstruct with string kernels . Additionally , there is substantial
D10-1023 alignment , alignment kernels , and string kernels . 2 Corpus Information We use
D09-1029 subsequences . The associated string kernel is defined by Kn ( si , sj )
D10-1023 learning -- sequence alignment and string kernels , as well as the introduction
D09-1112 will be referred as SK1 and SK2 ( String Kernel 1 and 2 ) . They differer in
D09-1142 kernel ridge regression and the string kernel ( KRR-SK ) presented in Section
D09-1142 whether that is the case , we use a string kernel , which for a given string (
D09-1112 between si and sj . Since the string kernel skips some elements of the target
C04-1109 , 2002 ) compared a wordbased string kernel and n-gram kernels at the sequence
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