Título:
|
A Hierarchical Approach for Multi-task Logistic Regression
|
Autor/a:
|
Lapedriza Garcia, Àgata; Masip Rodo, David; Vitrià, Jordi
|
Abstract:
|
Peer-reviewed |
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. |
Materia(s):
|
-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 |
Derechos:
|
https://creativecommons.org/licenses/by-nc-nd/2.5/es/ |
Tipo de documento:
|
Capítulo o parte de libro |
Compartir:
|
|