PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Efficient Algorithms for Similarity Measures over Sequential Data: A Look Beyond Kernels
Konrad Rieck, Pavel Laskov and Klaus-Robert Müller
In: DAGM 2006, 12-14 Sep 2006, Berlin, Germany.

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.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:2779
Deposited By:Pavel Laskov
Deposited On:22 November 2006