Learning performance of regularized moving least square regression

Hongzhi Tong; Qiang Wu

doi:10.1016/j.cam.2017.04.046

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Learning performance of regularized moving least square regression

Hongzhi Tong Qiang Wu

Year: 2017 Journal: Journal of Computational and Applied Mathematics Vol: 325 Pages: 42-55 Publisher: Elsevier BV

DOI: 10.1016/j.cam.2017.04.046

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Keywords:

Mathematics Representer theorem Regularization (linguistics) Hilbert space Square (algebra) Kernel (algebra) Stability (learning theory) Reproducing kernel Hilbert space Regression Applied mathematics Mean squared error Convergence (economics) Kernel method Algorithm Regression analysis Kernel principal component analysis Mathematical analysis Artificial intelligence Statistics Support vector machine Discrete mathematics Computer science Machine learning Geometry

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Sparse and Compressive Sensing Techniques

Physical Sciences → Engineering → Computational Mechanics

Numerical methods in inverse problems

Physical Sciences → Mathematics → Mathematical Physics

Machine Learning and ELM

Physical Sciences → Computer Science → Artificial Intelligence

Learning performance of regularized moving least square regression

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