Computational Intelligence (CI) is an interdisciplinary field comprising Neural Networks, Fuzzy Systems, Evolutionary Algorithms and their hybrid paradigms. It is a methodology involving computing that exhibits an ability to learn and/or to deal with new situations, such that the system is perceived to possess intelligent behavior such as generalization, discovery, association and abstraction. CI is a wide concept that can be applied to a large number of fields, including but not limited to complex systems modeling, diagnosis, prediction, control and information processing. The main areas of application include computer science and informatics, engineering, finance, bioinformatics and medicine.
“Computational Intelligence: Neural Networks and Kernel Methods” was chosen to be the topic of this seminar since the main part of research work developed by the author has been in this area, as it is clearly shown by checking her publications. Beyond the presentation of the past and present research in computational learning, this seminar presents also the view of the author regarding the main future lines of the area.
This seminar is intended for researchers, and doctoral and master students that are interested in the topic. Previous knowledge of learning theory, modeling, decision making and optimization is not mandatory, but it would help to understand the contents presented.
Next section presents the scope of computational intelligence, namely, neural networks and kernel methods, as defined by the most relevant international organizations and scientific journals in the area. The chapter ends with a summary of the topics covered in this seminar, including the perspectives of future lines of research in the area.