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Generalization performance of support vector classifiers for density level detection
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DOI：10.1016/j.neucom.2013.03.014.png)
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This paper investigates the generalization performance of support vector classifiers for density level detection (DLD) when the input term belongs to a separable Hilbert space. The estimate of learning rate for OLD problem is established by Rademacher average and iterative techniques, which is independent of the assumption of covering number used in the previous literature. (C) 2013 Elsevier B.V. All rights reserved.
Keyword:
Learning rate
Density level detection
Rademacher average
Iterative technique
期刊
IF:
6.5
论文数:
2.5W
被引数:
6.5W
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OAAI分享收藏Learning rates of support vector machine classifier for density level detection用于密度水平检测的支持向量机分类器的学习率.png)
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