JOURNAL ARTICLE
Online regression with unbounded sampling
Year: 2011 Journal: International Journal of Computer Mathematics Vol: 88 (14)Pages: 2936-2941 Publisher: Taylor & Francis
Abstract
Abstract In this paper, we consider a kernel-based online learning algorithm for regression when the sampling process is unbounded. Under a moment hypothesis on the sampling outputs, we provide a confidence-based bound for the error in the corresponding reproducing kernel Hilbert space. Keywords: learning theoryonline learningregressionreproducing kernel Hilbert spacesunbounded sampling process 2010 AMS Subject Classifications : 68T0562J02
Keywords:
Reproducing kernel Hilbert space Mathematics Kernel (algebra) Sampling (signal processing) Moment (physics) Kernel regression Regression Kernel embedding of distributions Kernel method Statistics Hilbert space Artificial intelligence Algorithm Applied mathematics Computer science Support vector machine Discrete mathematics Mathematical analysis
Metrics
3
Cited By
0.00
FWCI (Field Weighted Citation Impact)
13
Refs
0.14
Citation Normalized Percentile
Is in top 1%
Is in top 10%
Citation History
Topics
Advanced Bandit Algorithms Research
Social Sciences → Decision Sciences → Management Science and Operations Research
Machine Learning and Algorithms
Physical Sciences → Computer Science → Artificial Intelligence
Distributed Sensor Networks and Detection Algorithms
Physical Sciences → Computer Science → Computer Networks and Communications
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