Ensemble Prediction by Partial Matching
نویسنده
چکیده
Prediction by Partial Matching (PPM) is a lossless compression algorithm which consistently performs well on text compression benchmarks. This paper introduces a new PPM implementation called PPM-Ens which uses unbounded context lengths and ensemble voting to combine multiple contexts. The algorithm is evaluated on the Calgary corpus. The results indicate that combining multiple contexts leads to an improvement in the compression performance of PPM-Ens, although it does not outperform state of the art compression techniques.
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