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2008 JMLR JMLR 2008

Nearly Uniform Validation Improves Compression-Based Error Bounds

Abstract

This paper develops bounds on out-of-sample error rates for support vector machines (SVMs). The bounds are based on the numbers of support vectors in the SVMs rather than on VC dimension. The bounds developed here improve on support vector counting bounds derived using Littlestone and Warmuth's compression-based bounding technique. [abs] [ pdf ][ bib ] © JMLR 2008. (edit, beta)

🧭 Keyword Pioneer — compression-based bound
🐣 Hot Topic Early Bird — generalization bound
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Eric Bax

Topics

Machine Learning > Core Methods > Classification Machine Learning > Optimization & Theory > Learning Theory

Keywords

vc dimension support vector machine generalization bound compression-based bound
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