Publication: Hide
| dc.contributor.author | Kannika Khompurngson | en_US |
| dc.contributor.author | Charles A. Micchelli | en_US |
| dc.contributor.other | Mahidol University | en_US |
| dc.contributor.other | University at Albany State University of New York | en_US |
| dc.contributor.other | City University of Hong Kong | en_US |
| dc.date.accessioned | 2018-05-03T08:19:47Z | |
| dc.date.available | 2018-05-03T08:19:47Z | |
| dc.date.issued | 2011-10-31 | en_US |
| dc.description.abstract | In this paper we explore some aspects of the Hypercircle Inequality (Hi) in the context of kernel-based machine learning. We briefly describe Hi and its potential relevance to kernel- based learning when the data is known exactly and then extend it to circumstances where there is known data error (Hide). © 2011 Universidad de Jaén. | en_US |
| dc.identifier.citation | Jaen Journal on Approximation. Vol.3, No.1 (2011), 87-115 | en_US |
| dc.identifier.issn | 19897251 | en_US |
| dc.identifier.issn | 18893066 | en_US |
| dc.identifier.other | 2-s2.0-81855217448 | en_US |
| dc.identifier.uri | https://repository.li.mahidol.ac.th/handle/20.500.14594/12135 | |
| dc.rights | Mahidol University | en_US |
| dc.rights.holder | SCOPUS | en_US |
| dc.source.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=81855217448&origin=inward | en_US |
| dc.subject | Mathematics | en_US |
| dc.title | Hide | en_US |
| dc.type | Article | en_US |
| dspace.entity.type | Publication | |
| mu.datasource.scopus | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=81855217448&origin=inward | en_US |