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Hybridization and optimization of machine learning techniques for improved forecasting in real-world scenarios
dc.contributor.author | Stoean, Ruxandra | |
dc.date.accessioned | 2017-02-14T12:22:19Z | |
dc.date.available | 2017-02-14T12:22:19Z | |
dc.date.created | 2017 | |
dc.date.issued | 2017-02-14 | |
dc.identifier.uri | http://hdl.handle.net/10630/13070 | |
dc.description.abstract | Different and powerful machine learning paradigms are constantly in a race for delivering the lowest error and/or the highest comprehensibility. But what can certainly lead to better forecasting is model inter-cooperation or intra-optimization. The aim of the current talk is to put forward some recent ideas for such hybridization and optimization. Demonstrative experiments are outlined for problems coming from real, challenging environments. | es_ES |
dc.description.sponsorship | Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech. | es_ES |
dc.language.iso | eng | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject | Optimización matemática | es_ES |
dc.title | Hybridization and optimization of machine learning techniques for improved forecasting in real-world scenarios | es_ES |
dc.type | info:eu-repo/semantics/conferenceObject | es_ES |
dc.centro | Escuela de Ingenierías Industriales | es_ES |
dc.relation.eventdate | 19/07/2017 | es_ES |
dc.identifier.orcid | http://orcid.org/0000-0002-9849-5712 | es_ES |
dc.cclicense | by-nc-nd | es_ES |