Hybridization and optimization of machine learning techniques for improved forecasting in real-world scenarios
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.
Full text
CONFERENCIA DEPARTAMENTO DE MATEMÁTICA APLICADA UNIVERSIDAD DE MÁLAGA Name Ruxandra Stoean Title HybridizaBon and opBmizaBon of machine learning techniques for improved forecasBng in real-world scenarios Es+mated date 19th of July Short 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 beWer forecasBng is model inter-cooperaBon or intra-opBmizaBon. The aim of the current talk is to put forward some recent ideas for such hybridizaBon and opBmizaBon. DemonstraBve experiments are outlined for problems coming from real, challenging environments.