TL;DR
This paper proves that any concept class with VC-dimension d can be compressed into a sample of size d, resolving a longstanding conjecture in learning theory.
Contribution
It establishes that the minimal sample compression size for VC classes equals their VC-dimension, confirming a key conjecture.
Findings
Sample compression scheme size equals VC-dimension for any concept class
Resolved the Littlestone-Warmuth conjecture on VC class compression
Provides theoretical foundation for data compression in learning models
Abstract
Resolving a conjecture of Littlestone and Warmuth, we show that any concept class of VC-dimension has a sample compression scheme of size .
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Taxonomy
TopicsComputability, Logic, AI Algorithms · Mathematical Analysis and Transform Methods · Mathematical Dynamics and Fractals