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Fast kernels for string and tree matching AbstractIn this chapter we present a new algorithm suitable for matching discrete objects such as strings and trees in linear time, thus obviating dynamic programming with quadratic time complexity. This algorithm can be extended in various ways to provide linear time prediction cost in the length of the sequence to be classified. We demonstrate extensions in the case of position dependent weights, sliding window classifiers for a long sequence, and efficient algorithms for dealing with weights given in the form of dictionaries. This improvement on the currently available algorithms makes string kernels a viable alternative for the practitioner.
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