NDLI: Lattice sampling for efficient learning with Nadaraya-Watson local models

Content Provider	IEEE Xplore Digital Library
Author	Cervellera, C. Gaggero, M. Maccio, D. Marcialis, R.
Copyright Year	2014
Description	Author affiliation: Inst. of Intell. Syst. for Autom., Genoa, Italy (Cervellera, C.; Gaggero, M.; Maccio, D.; Marcialis, R.)
Abstract	The classical machine learning problem of estimating an unknown function through an empirical risk minimization (ERM) procedure is addressed when models based on local evaluation of the output are employed and there is freedom to sample the input space according to some deterministic rule. The combined use of lattice point sets, commonly employed for numerical integration, and local models based on kernel smoothers of the Nadaraya-Watson kind are analyzed regarding consistency of the ERM procedure. It is proved that the regular structure of lattice sampling guarantees the latter with good convergence rates. Furthermore, it is shown how the regular structure allows also practical advantages, like fast computation of the model output. Simulation tests are presented to showcase the behavior of Nadaraya-Watson models with lattice sampling in various function learning problems.
Starting Page	1915
Ending Page	1922
File Size	512168
Page Count	8
File Format	PDF
ISSN	21614407
e-ISBN	9781479914845
DOI	10.1109/IJCNN.2014.6889758
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-07-06
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Lattices Kernel Vectors Convergence Context Computational modeling Training
Content Type	Text
Resource Type	Article

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