A robust approach to empirical PDF estimate
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Jo, Sungho | ko |
| dc.date.accessioned | 2013-03-06T19:27:52Z | - |
| dc.date.available | 2013-03-06T19:27:52Z | - |
| dc.date.created | 2012-02-06 | - |
| dc.date.created | 2012-02-06 | - |
| dc.date.issued | 2005-08 | - |
| dc.identifier.citation | NEUROCOMPUTING, v.67, pp.288 - 296 | - |
| dc.identifier.issn | 0925-2312 | - |
| dc.identifier.uri | http://hdl.handle.net/10203/88156 | - |
| dc.description.abstract | This paper presents a robust approach to estimate the probability density function (PDF) from a sample data set. The approach is induced from entropy maximization using Renyi's quadratic entropy, and turns out to be equivalent to the support vector machines (SVM). Therefore, the approach has good properties of the support vector machines as a statistical function estimation method. (c) 2005 Published by Elsevier B.V. | - |
| dc.language | English | - |
| dc.publisher | ELSEVIER SCIENCE BV | - |
| dc.title | A robust approach to empirical PDF estimate | - |
| dc.type | Article | - |
| dc.identifier.wosid | 000231436300013 | - |
| dc.identifier.scopusid | 2-s2.0-21744436341 | - |
| dc.type.rims | ART | - |
| dc.citation.volume | 67 | - |
| dc.citation.beginningpage | 288 | - |
| dc.citation.endingpage | 296 | - |
| dc.citation.publicationname | NEUROCOMPUTING | - |
| dc.identifier.doi | 10.1016/j.neucom.2005.01.005 | - |
| dc.contributor.localauthor | Jo, Sungho | - |
| dc.type.journalArticle | Article | - |
| dc.subject.keywordAuthor | Renyi&apos | - |
| dc.subject.keywordAuthor | s quadratic entropy | - |
| dc.subject.keywordAuthor | support vector machines | - |
| dc.subject.keywordAuthor | probability density function | - |
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