Total stability of kernel methods
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- 所属单位:数理与信息工程学院
- 文献类型:期刊
- 发表时间:2018-01-01
- 发表刊物:Neurocomputing
- 卷号:Vol.289
- 页面范围:101-118
- Issn号:0925-2312
- 是否译文:否
- 关键字:Machine;learning;Stability;Robustness;Kernel;Regularization
- 摘要:Regularized empirical risk minimization using kernels and their corresponding reproducing kernel Hilbert spaces (RKHSs) plays an important role in machine learning. However, the actually used kernel often depends on one or on a few hyperparameters or the
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