2026-05-27T05:36:30Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/130702026-02-03T11:46:36Zcom_10630_2254col_10630_37959

Stoean, Ruxandra 2017-02-14T12:22:19Z 2017-02-14T12:22:19Z 2017-02-14 http://hdl.handle.net/10630/13070 http://orcid.org/0000-0002-9849-5712 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. eng open access by-nc-nd Optimización matemática Hybridization and optimization of machine learning techniques for improved forecasting in real-world scenarios conference output