An Introduction to Compression-Based Machine Learning

2026-09-18

AI Summary

论文探讨了如何通过压缩算法转换成机器学习方法，利用归一化压缩距离或最小描述长度原则解决AI中的问题，并提出了一种基于压缩的机器学习设计框架。

Abstract

Any lossless compression algorithm (like gzip) may be converted into a machine learning method, via either Normalized Compression Distance or the Minimum Description Length principle. Any auto-regressive model may be converted into a lossless compression method via entropy coding. This seemingly circular dependence has unrealized potential in modern artificial intelligence and machine learning, and we survey and formalize the various strategies that have been used to leverage compression for machine learning. We introduce and empirically validate a design framework for compression-based ML, finding compression-based methods competitive with conventional baselines and decisively stronger on malware. We find that varying these design choices yields accuracy gains of up to 0.62.

Problem

• compression-based machine learning
• lossless compression
• machine learning
• malware detection

Innovation

• Compression-based Machine Learning
• Normalized Compression Distance
• Minimum Description Length
• Entropy Coding
• Malware Detection