Para acceder a los documentos con el texto completo, por favor, siga el siguiente enlace: http://hdl.handle.net/10609/1378

A Hierarchical Approach for Multi-task Logistic Regression
Lapedriza Garcia, Àgata; Masip Rodó, David; Vitrià, Jordi
Peer-reviewed
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 regressionmodel and develop a learning algorithm based in this framework, which can deal with the small sample size problem. Our experiments performedin two independent databases from the UCI and a multi-task face classification experiment show the improved accuracies of the multi-tasklearning approach with respect to the single task approach when using the same probabilistic model.
Computer software -- Development
Pattern recognition systems
Logistic regression analysis
Programari -- Desenvolupament
Reconeixement de formes (Informàtica)
Anàlisi de regressió
Regressió logística
Software -- Desarrollo
Reconocimiento de formas (Informática)
Análisis de regresión logística
Consulteu les condicions d'ús d'aquest document en el repositori original:http://hdl.handle.net/10609/1378
Part of book or chapter of book
         

Mostrar el registro completo del ítem

Documentos relacionados

Otros documentos del mismo autor/a

Lapedriza Garcia, Àgata; Seguí, Santi; Masip Rodó, David; Vitrià, Jordi
Masip Rodó, David; Lapedriza Garcia, Àgata; Vitrià, Jordi
Masip Rodó, David; Lapedriza Garcia, Àgata; Vitrià, Jordi
Masip Rodó, David; Lapedriza Garcia, Àgata; Vitrià, Jordi