On Prediction Using Variable Order Markov Models - Companion Site
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Abstract
This is a companion site for the paper ``On Prediction Using Variable Order Markov Models'' by Begleiter, El-Yaniv and Yona. The paper is concerned with algorithms for prediction of discrete sequences over a finite alphabet, using variable order Markov models. We discuss, in the paper, the properties of six prominent prediction algorithms and compare their performance using real life sequences from three domains: proteins, English text and music pieces. The comparison is made with respect to prediction quality as measured by the average log-loss. We also compare classification algorithms based on these predictors with respect to a number of large protein classification tasks.
In this site we provide: in the code section, full source code of the six VMM algorithms, and in the datasets section links to the datasets we used.
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