"Development of computational intelligence methods with emphasis on adaptive techniques for big data"

The objective of this dissertation is the development of computational intelligence methods with increased efficiency, in order to be able to deal with large-scale data; emphasis will be given on neural network training algorithms. Big Data analytics is increasingly receiving attention and importance, as it holds unique promises, but on the other hand, it is characterized by numerous computational and statistical challenges. The improvement and further development of conventional and non-conventional methods, together with the corresponding adaptive algorithms will be crucial for the proper management of large-scale data. The produced models will be tested on large-scale data, coming from various scientific areas, in order to evaluate their ability to handle Big Data challenges, aiming at both statistical accuracy and computational efficiency for real-world applications.