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REGULARIZATION IN KERNEL LEARNING
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DOI：10.1214/09-AOS728.png)
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摘要
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Under mild assumptions on the kernel, we obtain the best known error rates in a regularized learning scenario taking place in the corresponding reproducing kernel Hilbert space (RKHS). The main novelty in the analysis is a proof that one can use a regularization term that grows significantly slower than the standard quadratic growth in the RKHS norm.
Keyword:
Regression
reproducing kernel Hilbert space
regulation
least-squares
model selection
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3.7
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2.8K
被引数:
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