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The Entire Regularization Path for the Support Vector Machine

Conference Paper Artificial Intelligence ยท Machine Learning

Abstract

In this paper we argue that the choice of the SVM cost parameter can be critical. We then derive an algorithm that can fit the entire path of SVM solutions for every value of the cost parameter, with essentially the same computational cost as fitting one SVM model.

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Keywords

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Context

Venue
Annual Conference on Neural Information Processing Systems
Archive span
1987-2025
Indexed papers
30776
Paper id
409330865659384786
v2026.09.13