@inproceedings{95fbcc619d20482e84d1520fe8636d50,

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",

isbn = "978-3-540-40813-0",

series = "Lecture Notes in Computer Science",

publisher = "Springer-Verlag GmbH",

pages = "134--142",

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

booktitle = "Advances in Intelligent Data Analysis V",

}