J. López Fidalgo, J. A. Moler Cuiral, D. P. Wiens

We aim to develop a theory of model-robust classification, and a methodology for applying this to large data sets such as arise in machine learning. The general idea is that there is a (large) population of explanatory variables, which can be easily sampled. With probability a(x; t), an item with covariates x belongs to group A and with probability 1 - a(x; t) it belongs to group .B.. We suppose that the determination of the appropriate group, given x, is difficult and expensive, so that the investigator wishes to sample from x in a manner which is more efficient than random sampling (sometimes termed .passive learning.).

Keywords: Big data, logistic regression, optimal experimental design, robust models


GT7-1 Design of Experiments
September 3, 2019  3:30 PM
I2L5. Georgina Blanes building

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Diseño Óptimo de Experimentos para la Ecuación de Antoine en experimentos de destilación

C. de la Calle Arroyo, J. López-Fidalgo, L. Rodríguez-Aragón

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