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Title: | A Hierarchical Approach for Multi-task Logistic Regression |
Author: | Lapedriza, Agata Masip Rodó, David Vitrià Marca, Jordi |
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 |
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. |
DOI: | 10.1007/978-3-540-72849-8_33 |
Document type: | info:eu-repo/semantics/bookPart |
Issue Date: | 2007 |
Publication license: | https://creativecommons.org/licenses/by-nc-nd/2.5/es/ |
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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