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DC Field | Value | Language |
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dc.contributor.author | Lapedriza, Agata | - |
dc.contributor.author | Masip Rodó, David | - |
dc.contributor.author | Vitrià Marca, Jordi | - |
dc.date.accessioned | 2010-02-16T11:57:39Z | - |
dc.date.available | 2010-02-16T11:57:39Z | - |
dc.date.issued | 2007 | - |
dc.identifier.citation | LAPEDRIZA, A.; MASIP, D.; VITRIÀ, J. (2007). "A Hierarchical Approach for Multi-task Logistic Regression". In: MARTÍ, J.; BENEDI, J.M.; MENDONÇA, A.M.; SERRAT, J. Lecture Notes in Computer Science. Springer. Núm. 4478. Pág. 258-265 | - |
dc.identifier.issn | 0302-9743MIAR | - |
dc.identifier.uri | http://hdl.handle.net/10609/1378 | - |
dc.description | Peer-reviewed | - |
dc.description.abstract | In the statistical pattern recognition eld the number of samples to train a classifer is usually insu cient. Nevertheless, it has been shown that some learning domains can be divided in a set of related tasks, that can be simultaneously trained sharing information among the different tasks. This methodology is known as the multi-task learning paradigm. In this paper we propose a multi-task probabilistic logistic regression model and develop a learning algorithm based in this framework, which can deal with the small sample size problem. Our experiments performed in two independent databases from the UCI and a multi-task face classification experiment show the improved accuracies of the multi-task learning approach with respect to the single task approach when using the same probabilistic model. | en |
dc.language.iso | eng | - |
dc.relation.ispartof | Computer Science, Technology and Multimedia | en |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/2.5/es/ | - |
dc.subject.lcsh | Computer software -- Development | en |
dc.subject.lcsh | Pattern recognition systems | en |
dc.subject.lcsh | Logistic regression analysis | en |
dc.title | A Hierarchical Approach for Multi-task Logistic Regression | - |
dc.type | info:eu-repo/semantics/bookPart | - |
dc.audience.mediator | Theme areas::Computer Science, Technology and Multimedia | en |
dc.subject.lemac | Programari -- Desenvolupament | ca |
dc.subject.lemac | Reconeixement de formes (Informàtica) | ca |
dc.subject.lemac | Anàlisi de regressió | ca |
dc.subject.lemac | Regressió logística | ca |
dc.subject.lcshes | Software -- Desarrollo | es |
dc.subject.lcshes | Reconocimiento de formas (Informática) | es |
dc.subject.lcshes | Análisis de regresión logística | es |
dc.identifier.doi | 10.1007/978-3-540-72849-8_33 | - |
Appears in Collections: | Capítols o parts de llibres |
Files in This Item:
File | Description | Size | Format | |
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Lapedriza_LNCS2007_Hierarchical.pdf | 131,7 kB | Adobe PDF | View/Open |
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