Browsing by Author "b4ddc9e8-4617-4233-af82-a2985b0c0328"
Now showing items 1-7 of 7
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Maldonado, Sebastián; Montoya Moreira, Ricardo; Weber, Richard (Elsevier, 2015)One of the main tasks of conjoint analysis is to identify consumer preferences about potential products or services. Accordingly, different estimation methods have been proposed to determine the corresponding relevant ...
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Maldonado, Sebastián; Armelini, Guillermo; Guevara Cue, Cristián (IOS Press, 2017)Recruiting prospective students efficiently and effectively is a very important challenge for universities, mainly because of the increasing competition and the relevance of enrollment-generated revenues. This work provides ...
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Maldonado, Sebastián; Montoya Moreira, Ricardo; López, Julio (Springer, 2017)This paper presents a novel embedded feature selection approach for Support Vector Machines (SVM) in a choice-based conjoint context. We extend the L1-SVM formulation and adapt the RFE-SVM algorithm to conjoint analysis ...
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Aguilar Rivera, Marcelo; Kim, Sanggyun; Coleman, Todd P.; Maldonado, Pedro E.; Torrealba, Fernando (Nature, 2020)The insular cortex plays a central role in the perception and regulation of bodily needs and emotions. Its modular arrangement, corresponding with different sensory modalities, denotes a complex organization, and reveals ...
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Maldonado, Sebastián; Merigó Lindahl, José; Miranda Pino, Jaime (Elsevier, 2018)In this work, the classical soft-margin Support Vector Machine (SVM) formulation is redefined with the inclusion of an Ordered Weighted Averaging (OWA) operator. In particular, the hinge loss function is rewritten as a ...
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Maldonado, Sebastián; López, Julio; Jiménez Molina, Ángel; Lira, Hernán (Elsevier, 2020)In this study, an expert system is presented for analyzing the mental workload of interacting with a mobile phone while facing common daily tasks. Psychophysiological signals were collected from various devices, each ...
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López, Julio; Maldonado, Sebastián; Montoya Moreira, Ricardo (Palgrave Macmillan Ltd., 2017)Support vector machines (SVMs) have been successfully used to identify individuals' preferences in conjoint analysis. One of the challenges of using SVMs in this context is to properly control for preference heterogeneity ...