Very predictive ngrams for space-limited probabilistic models

Paul R. Cohen, Charles A. Sutton

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMichael R. Berthold, Hans-Joachim Lenz, Elizabeth Bradley, Rudolf Kruse, Christian Borgelt
PublisherSpringer-Verlag
Pages134-142
Number of pages9
ISBN (Print)3540408134, 9783540408130
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2810
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Very predictive ngrams for space-limited probabilistic models'. Together they form a unique fingerprint.

Cite this