Performance analysis of the LapRSSLG algorithm in learning theory
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Publication:5220067
DOI10.1142/S0219530519410033zbMath1432.68399OpenAlexW2982298999WikidataQ126856521 ScholiaQ126856521MaRDI QIDQ5220067
Bao Huai Sheng, Haizhang Zhang
Publication date: 10 March 2020
Published in: Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1142/s0219530519410033
Learning and adaptive systems in artificial intelligence (68T05) Hilbert spaces with reproducing kernels (= (proper) functional Hilbert spaces, including de Branges-Rovnyak and other structured spaces) (46E22)
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Cites Work
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- Learning sparse gradients for variable selection and dimension reduction
- Convergence rate of the semi-supervised greedy algorithm
- Consistency of regularized spectral clustering
- Learning gradients on manifolds
- Derivative reproducing properties for kernel methods in learning theory
- The covering number in learning theory
- Generalization errors of Laplacian regularized least squares regression
- Learning sets with separating kernels
- Consistency and robustness of kernel-based regression in convex risk minimization
- Learning gradients by a gradient descent algorithm
- On the mathematical foundations of learning
- Nonparametric sparsity and regularization
- The performance of semi-supervised Laplacian regularized regression with the least square loss
- Covering Numbers for Convex Functions
- Learning Theory
- ESTIMATING THE APPROXIMATION ERROR IN LEARNING THEORY
- 10.1162/1532443041827925
- Theory of Reproducing Kernels
- Convex analysis and monotone operator theory in Hilbert spaces
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