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- W1805472169 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." @default.
- W1805472169 created "2016-06-24" @default.
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- W1805472169 date "2003-01-01" @default.
- W1805472169 modified "2023-09-25" @default.
- W1805472169 title "Very Predictive Ngrams for Space-Limited Probabilistic Models" @default.
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- W1805472169 doi "https://doi.org/10.1007/978-3-540-45231-7_13" @default.
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