Sequential function approximation with noisy data

Cited 4 time in webofscience Cited 0 time in scopus
  • Hit : 46
  • Download : 0
We present a sequential method for approximating an unknown function sequentially using random noisy samples. Unlike the traditional function approximation methods, the current method constructs the approximation using one sample at a time. This results in a simple numerical implementation using only vector operations and avoids the need to store the entire data set. The method is thus particularly suitable when data set is exceedingly large. Furthermore, we present a general theoretical framework to define and interpret the method. Both upper and lower bounds of the method are established for the expectation of the results. Numerical examples are provided to verify the theoretical findings. (C) 2018 Elsevier Inc. All rights reserved.
Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
Issue Date
2018-10
Language
English
Article Type
Article
Citation

JOURNAL OF COMPUTATIONAL PHYSICS, v.371, pp.363 - 381

ISSN
0021-9991
DOI
10.1016/j.jcp.2018.05.042
URI
http://hdl.handle.net/10203/297252
Appears in Collection
MA-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 4 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0