Linear-Time Computation of Similarity Measures for Sequential Data

Abstract

Efficient and expressive comparison of sequences is an essential procedure for learning with sequential data. In this article we propose a generic framework for computation of similarity measures for sequences, covering various kernel, distance and non-metric similarity functions. The basis for comparison is embedding of sequences using a formal language… (More)
DOI: 10.1145/1390681.1390683

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