@inproceedings{7d86c32e28f6426cae9ed45ed4108d98,
title = "Efficient algorithms for similarity measures over sequential data: A look beyond kernels",
abstract = "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.",
author = "Konrad Rieck and Pavel Laskov and M{\"u}ller, {Klaus Robert}",
year = "2006",
doi = "10.1007/11861898_38",
language = "English",
isbn = "3540444122",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "374--383",
booktitle = "Pattern Recognition - 28th DAGM Symposium, Proceedings",
note = "28th Symposium of the German Association for Pattern Recognition, DAGM 2006 ; Conference date: 12-09-2006 Through 14-09-2006",
}