2026-05-30T03:26:38Zhttps://riuma.uma.es/rest/oai/request

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

Hybridization and optimization of machine learning techniques for improved forecasting in real-world scenarios Stoean, Ruxandra Optimización matemática 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. 2017-02-14T12:22:19Z 2017-02-14T12:22:19Z 2017 2017-02-14 conference output http://hdl.handle.net/10630/13070 http://orcid.org/0000-0002-9849-5712 eng 19/07/2017 open access by-nc-nd