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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Main Authors:	Kannika Khompurngson, Suthep Suantai
Format:	Journal
Published:	2018
Subjects:	Mathematics
Online Access:	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84985964605&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/55944
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Institution:	Chiang Mai University

id	th-cmuir.6653943832-55944
record_format	dspace
spelling	th-cmuir.6653943832-559442018-09-05T03:06:15Z Alternative approximation method for learning multiple feature Kannika Khompurngson Suthep Suantai Mathematics © 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. 2018-09-05T03:06:15Z 2018-09-05T03:06:15Z 2016-08-01 Journal 16860209 2-s2.0-84985964605 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84985964605&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/55944
institution	Chiang Mai University
building	Chiang Mai University Library
country	Thailand
collection	CMU Intellectual Repository
topic	Mathematics
spellingShingle	Mathematics Kannika Khompurngson Suthep Suantai Alternative approximation method for learning multiple feature
description	© 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.
format	Journal
author	Kannika Khompurngson Suthep Suantai
author_facet	Kannika Khompurngson Suthep Suantai
author_sort	Kannika Khompurngson
title	Alternative approximation method for learning multiple feature
title_short	Alternative approximation method for learning multiple feature
title_full	Alternative approximation method for learning multiple feature
title_fullStr	Alternative approximation method for learning multiple feature
title_full_unstemmed	Alternative approximation method for learning multiple feature
title_sort	alternative approximation method for learning multiple feature
publishDate	2018
url	https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84985964605&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/55944
_version_	1681424599860903936

Alternative approximation method for learning multiple feature

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