@inbook{3a30c0255beb4045a90476879a80ebff,

title = "Very predictive ngrams for space-limited probabilistic models",

abstract = "In sequential prediction tasks, one repeatedly tries to predict the next element in a sequence. A classical way to solve these problems is to fit an order-n Markov model to the data, but fixed-order models are often bigger than they need to be. In a fixed-order model, all predictors are of length n, even if a shorter predictor would work just as well. We present a greedy algorithm, VPR, for finding variable-length predictive rules. Although VPR is not optimal, we show that on English text, it performs similarly to fixed-order models but uses fewer parameters.",

author = "Cohen, {Paul R.} and Sutton, {Charles A.}",

year = "2003",

doi = "10.1007/978-3-540-45231-7_13",

language = "English (US)",

isbn = "3540408134",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer-Verlag",

pages = "134--142",

editor = "Berthold, {Michael R.} and Hans-Joachim Lenz and Elizabeth Bradley and Rudolf Kruse and Christian Borgelt",

booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

}