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• W3098226149 abstract "We analyze a family of supervised learning algorithms based on sample compression schemes that are stable, in the sense that removing points from the training set which were not selected for the compression set does not alter the resulting classifier. We use this technique to derive a variety of novel or improved data-dependent generalization bounds for several learning algorithms. In particular, we prove a new margin bound for SVM, removing a log factor. The new bound is provably optimal. This resolves a long-standing open question about the PAC margin bounds achievable by SVM." @default.
• W3098226149 created "2020-11-23" @default.
• W3098226149 creator A5019371046 @default.
• W3098226149 creator A5074158209 @default.
• W3098226149 date "2020-11-09" @default.
• W3098226149 modified "2023-09-27" @default.
• W3098226149 title "Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Bound" @default.
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• W3098226149 hasPublicationYear "2020" @default.
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