A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2007; you can also visit the original URL.
The file type is
Lecture Notes in Computer Science
Kernel functions as similarity measures for sequential data have been extensively studied in previous research. This contribution addresses the efficient computation of distance functions and similarity coefficients for sequential data. Two proposed algorithms utilize different data structures for efficient computation and yield a runtime linear in the sequence length. Experiments on network data for intrusion detection suggest the importance of distances and even non-metric similarity measures for sequential data.doi:10.1007/11861898_38 fatcat:3oankd3oqraafakv63zqgaizmm