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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th-cmuir.6653943832-416802017-09-28T04:22:45Z Alternative approximation method for learning multiple feature Khompurngson K. Suantai S. © 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. 2017-09-28T04:22:45Z 2017-09-28T04:22:45Z 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/6653943832/41680 |
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© 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 |
Khompurngson K. Suantai S. |
| spellingShingle |
Khompurngson K. Suantai S. Alternative approximation method for learning multiple feature |
| author_facet |
Khompurngson K. Suantai S. |
| author_sort |
Khompurngson K. |
| 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 |
2017 |
| url |
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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1681422046805884928 |