We address the problem of improving variable-length-to- fixed-length codes (VF codes), which have favourable properties for fast compressed pattern matching but moderate compression ratios. Compression ratio of VF codes depends on the parse tree that is used as a dictionary. We propose a method that trains a parse tree by scanning an input text repeatedly, and we show experimentally that it improves the compression ratio of VF codes rapidly to the level of state-of-the-art compression methods. © 2010 Springer-Verlag.
CITATION STYLE
Uemura, T., Yoshida, S., Kida, T., Asai, T., & Okamoto, S. (2010). Training parse trees for efficient VF coding. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6393 LNCS, pp. 179–184). https://doi.org/10.1007/978-3-642-16321-0_17
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