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https://hdl.handle.net/10216/50012Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.creator | João M. Moreira | |
| dc.creator | Alípio M. Jorge | |
| dc.creator | Carlos Soares | |
| dc.creator | Jorge Freire de Sousa | |
| dc.date.accessioned | 2022-09-11T09:01:37Z | - |
| dc.date.available | 2022-09-11T09:01:37Z | - |
| dc.date.issued | 2006 | |
| dc.identifier.issn | 0302-9743 | |
| dc.identifier.other | sigarra:57994 | |
| dc.identifier.uri | https://hdl.handle.net/10216/50012 | - |
| dc.description.abstract | This paper describes the study on example selection in regression problems using mu-SVM (Support Vector Machine) linear as prediction algorithm. The motivation case is a study done on real data for a problem of bus trip time prediction. In this study we use three different training sets: all the examples, examples from past days similar to the day where prediction is needed, and examples selected by a CART regression tree. Then, we verify if the CART based example selection approach is appropriate on different regression data sets. The experimental results obtained are promising. | |
| dc.language.iso | eng | |
| dc.rights | openAccess | |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc/4.0/ | |
| dc.subject | Tecnologia dos transportes, Ciências da computação e da informação | |
| dc.subject | Transport technology, Computer and information sciences | |
| dc.title | Improving SVM-linear predictions using CART for example selection | |
| dc.type | Artigo em Revista Científica Internacional | |
| dc.contributor.uporto | Faculdade de Engenharia | |
| dc.contributor.uporto | Faculdade de Economia | |
| dc.identifier.doi | 10.1007/11875604_70 | |
| dc.identifier.authenticus | P-004-PPV | |
| dc.subject.fos | Ciências exactas e naturais::Ciências da computação e da informação | |
| dc.subject.fos | Natural sciences::Computer and information sciences | |
| Appears in Collections: | FEP - Artigo em Revista Científica Internacional FEUP - Artigo em Revista Científica Internacional | |
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