Alternative approximation method for learning multiple feature

© 2016 by the Mathematical Association of Thailand. All rights reserved. The theory of reproducing kernel Hilbert space (RKHS) has recently appeared as a powerful framework for the learning problem. The principal goal of the learning problem is to determine a functional which best describes given da...

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Bibliographic Details
Main Authors: Khompurngson K., Suantai S.
Format: Journal
Published: 2017
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84985964605&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/41680
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Institution: Chiang Mai University
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Summary:© 2016 by the Mathematical Association of Thailand. All rights reserved. The theory of reproducing kernel Hilbert space (RKHS) has recently appeared as a powerful framework for the learning problem. The principal goal of the learning problem is to determine a functional which best describes given data. Recently, we have extended the hypercircle inequality to data error in two ways: First, we have extended it to circumstance for which all data is known within error. Second, we have extended it to partially-corrupted data. That is, data set contains both accurate and inaccurate data. In this paper, we report on further computational experiments by using the material from both previous work.