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arXiv Machine Learning By John Hurwitz, Edward Raff, Charles K. Nicholas

An Introduction to Compression-Based Machine Learning

Read the original on arXiv Machine Learning →

The paper discusses how any lossless compression algorithm can be transformed into a machine learning method using Normalized Compression Distance or the Minimum Description Length principle, and conversely how any auto‑regressive model can become a lossless compressor via entropy coding. It surveys and formalizes these strategies, introduces a design framework for compression‑based ML, and empirically validates that such methods can match conventional baselines and outperform them on malware detection, achieving accuracy gains up to 0.62 by varying design choices.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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