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Please use this identifier to cite or link to this item: http://hdl.handle.net/10651/31232

Title: Analyzing sensory data using non-linear preference learning with feature subset selection
Author(s): Luaces Rodríguez
Fernández Bayón, Gustavo
Quevedo Pérez, José Ramón
Díez Peláez, Jorge
Coz Velasco, Juan José del
Bahamonde Rionda, Antonio
Issue date: 2004
Publisher: Springer
Publisher version: http://dx.doi.org/10.1007/978-3-540-30115-8_28
Format extent: p. 286-297
Abstract: The quality of food can be assessed from different points of view. In this paper, we deal with those aspects that can be appreciated through sensory impressions. When we are aiming to induce a function that maps object descriptions into ratings, we must consider that consumers’ ratings are just a way to express their preferences about the products presented in the same testing session. Therefore, we postulate to learn from consumers’ preference judgments instead of using an approach based on regression. This requires the use of special purpose kernels and feature subset selection methods. We illustrate the benefits of our approach in two families of real-world data bases
Description: 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004
URI: http://hdl.handle.net/10651/31232
ISBN: 978-3-540-23105-9
Appears in Collections:Ponencias, Discursos y Conferencias

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