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In the setting of supervised learning using reproducing kernel methods, we propose a data-dependent regularization parameter selection rule that is adaptive to the unknown regularity of the target function and is optimal both for the least-square (prediction) error and for the reproducing kernel Hilbert space (reconstruction) norm error.
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Convergence rates of general regularization methods for statistical inverse problems and applications
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Spectral algorithms for supervised learning
L. Lo Gerfo, L. Rosasco, F. Odone, E. De Vito, and A. Verri · 2008
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Shuai Lu and Sergei V Pereverzev · 2013
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Alessandro Rudi, Raffaello Camoriano, and Lorenzo Rosasco · 2015
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Learning theory of distributed spectral algorithms
Zheng-Chu Guo, Shao-Bo Lin, and Ding-Xuan Zhou · 2017
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Direct and Inverse Problems in Machine Learning: Kernel Methods and Spectral Regularization
Nicole Mücke · 2017
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Optimal rates for the regularized learning algorithms under general source condition
Abhishake Rastogi and Sivananthan Sampath · 2017
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I. Steinwart, D. Hush, and C. Scovel · 2009
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E. De Vito, S. Pereverzyev, and L. Rosasco · 2010
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Optimal rates for regularization of statistical inverse learning problems
Gilles Blanchard and Nicole Mücke · 2018
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Balancing principle in supervised learning for a general regularization scheme
Shuai Lu, Peter Mathé, and Sergei V. Pereverzev · 2018
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The Goldenshluger-Lepski Method for Constrained Least-Squares Estimators over RKHSs
Stephen Page and Steffen Grünewälder · 2018
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