Safe Sample Screening for Robust Support Vector Machine
نویسندگان
چکیده
منابع مشابه
Safe screening for support vector machines
The L2-regularized hinge loss kernel SVM could be the most important and most studied machine learning algorithm. Unfortunately, its computational training time complexity is generally unsuitable for big data. Empirical runtimes can however often be reduced using shrinking heuristics on the training sample set, which exploit the fact that non-support vectors do not affect the decision boundary....
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ژورنال
عنوان ژورنال: Proceedings of the AAAI Conference on Artificial Intelligence
سال: 2020
ISSN: 2374-3468,2159-5399
DOI: 10.1609/aaai.v34i04.6182