Journal article
A geometric approach to sample compression
BIP Rubinstein, JH Rubinstein
Journal of Machine Learning Research | MICROTOME PUBL | Published : 2012
Abstract
The Sample Compression Conjecture of Littlestone & Warmuth has remained unsolved for a quarter century. While maximum classes (concept classes meeting Sauer's Lemma with equality) can be compressed, the compression of general concept classes reduces to compressing maximal classes (classes that cannot be expanded without increasing VC dimension). Two promising ways forward are: embedding maximal classes into maximum classes with at most a polynomial increase to VC dimension, and compression via operating on geometric representations. This paper presents positive results on the latter approach and a first negative result on the former, through a systematic investigation of finite maximum class..
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Awarded by NSF
Funding Acknowledgements
We thank Peter Bartlett for his very helpful feedback, and gratefully acknowledge the support of the NSF through grants DMS-0434383 and DMS-0707060, and the support of the Siebel Scholars Foundation.