Probability bounds for active learning in the regression problem
Fecha
2018Autor
Fermin, Ana K.
Ludeña, Carenne
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Resumen
In this article we consider the problem of active learning in the regression setting.
That is, choosing an optimal sampling scheme for the regression problem
simultaneously with that of model selection. We consider a batch type approach
and an on–line approach adapting algorithms developed for the classification problem.
Our main tools are concentration–type inequalities which allow us to bound
the supreme of the deviations of the sampling scheme corrected by an appropriate
weight function.
Palabras clave
Probability bounds; Active learning; Regression problemEnlace al recurso
https://link.springer.com/chapter/10.1007/978-3-319-96941-1_14Colecciones
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