Superior Guarantees for Sequential Prediction and Lossless
Compression via Alphabet Decomposition
Ron Begleiter and Ran El-Yaniv
Journal of Machine Learning Research
We present worst case bounds for the learning rate of a known prediction method that is based on hierarchical applications of binary Context Tree Weighting (CTW) predictors.
A heuristic application of this approach that relies on Huffman's alphabet decomposition
is known to achieve state-of-the-art performance in prediction and lossless compression
benchmarks. We show that our new bound for this heuristic is tighter than the best known performance guarantees for prediction and lossless compression algorithms in various
settings. This result substantiates the e±ciency of this hierarchical method and provides a compelling explanation for its practical success. In addition, we present the results of a few experiments that examine other possibilities for improving the multi-alphabet prediction performance of CTW-based algorithms.