On extension theorems and their connection to universal consistency in machine learning

DOI10.1142/S0219530516400029zbMath1353.60035arXiv1604.04505MaRDI QIDQ2835986

Florian Dumpert, Dao-Hong Xiang, Andreas Christmann

Publication date: 30 November 2016

Published in: Analysis and Applications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1604.04505

zbMATH Keywords

machine learningreproducing kernel Hilbert spacedensenessuniversal consistencykernel learningLusin's theoremDugundji extension theorem

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Computational learning theory (68Q32) 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) Limit theorems in probability theory (60F99)

Related Items

Weighted p-regular kernels for reproducing kernel Hilbert spaces and Mercer Theorem

Uses Software

• SHOGUN
• SimpleMKL

Cites Work

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