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Convergence Rate of Coefficient Regularized Kernel-based Learning Algorithms |
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PP: 885-889 |
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Author(s) |
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Sheng Baohuai,
Ye Peixin,
Yu Wangke,
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Abstract |
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| We investigate machine learning for the least square regression with data dependent hypothesis and coefficient regularization
algorithms based on general kernels. We provide some estimates for the learning raters of both regression and classification when the
hypothesis spaces are sample dependent. Under a weak condition on the kernels we derive learning error by estimating the rate of some
K-functional when the target functions belong to the range of some Hilbert-Schmidt integral operator. |
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