LEARNING RATES OF REGULARIZED REGRESSION FOR FUNCTIONAL DATA

DOI10.1142/S0219691309003288zbMath1182.62003OpenAlexW2169163030MaRDI QIDQ5189981

Di-Rong Chen, Yong-Li Xu

Publication date: 11 March 2010

Published in: International Journal of Wavelets, Multiresolution and Information Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0219691309003288

zbMATH Keywords

learning theoryreproducing kernel Hilbert spaceregularized learning algorithm

Mathematics Subject Classification ID

General nonlinear regression (62J02) Learning and adaptive systems in artificial intelligence (68T05) Applications of functional analysis in probability theory and statistics (46N30)

Related Items (4)

LOCAL LEARNING ESTIMATES BY INTEGRAL OPERATORS ⋮ A WAVELET METHOD COUPLED WITH QUASI-SELF-SIMILAR STOCHASTIC PROCESSES FOR TIME SERIES APPROXIMATION ⋮ THE COEFFICIENT REGULARIZED REGRESSION WITH RANDOM PROJECTION ⋮ Randomized multi-scale kernels learning with sparsity constraint regularization for regression

Uses Software

• fda (R)

Cites Work

• Regression models for functional data by reproducing kernel Hilbert spaces methods
• Functional multi-layer perceptron: A nonlinear tool for functional data analysis
• Best choices for regularization parameters in learning theory: on the bias-variance problem.
• Regularization networks and support vector machines
• Learning theory: stability is sufficient for generalization and necessary and sufficient for consistency of empirical risk minimization
• Learning rates of least-square regularized regression
• Shannon sampling. II: Connections to learning theory
• Learning theory estimates via integral operators and their approximations
• On the mathematical foundations of learning
• Learning Theory

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