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Sampled Pattern Matching (SPM) definition

5th May 2007

“… consider a universal predictor based on pattern matching: Given a sequence X_i,… ,X_n drawn from a stationary mixing source, it predicts the next symbol X_n+i based on selecting a context of X_n+i. The predictor, called the Sampled Pattern Matching (SPM), is a modification of the Ehrenfeucht-Mycielski pseudo random generator algorithm. It predicts the value of the most frequent symbol appearing at the so called sampled positions. These positions follow the occurrences of a fraction of the longest suffix of the original sequence that has another copy inside X_iX₂ … X_n. In other words, in SPM the context selection consists of taking certain fraction of the longest match. The study of the longest match for lossless data compression was initiated by [Aaron D.] Wyner and Ziv in their 1989 seminal paper.”

This was an excerpt from a paper titled “A Universal Predictor Based on Pattern Matching” by Philippe Jacquet, Wojciech Szpankowski and Izydor Apostol. This appears to be the best definition of SPM I found.

I found the text of this article available in PostScript format, and assume it’s OK to duplicate it here in PDF format: A Universal Predictor Based on Pattern Matching. Please contact me if there is a need to remove this link.

This entry was posted on Saturday, May 5th, 2007 at 13:22 and is filed under Artificial Intelligence, Bioinformatics, Science. You can follow any responses to this entry through the RSS 2.0 feed. You can skip to the end and leave a response. Pinging is currently not allowed.

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