Abstract
Recently, increasing attention has been paid to the development of approximate algorithms for equipping machines with an automaton level of intelligence. The aim is to permit the implementation of intelligent behaving systems able to perform tasks which are just a human prerogative. In this context, neural network models have been privileged, thanks to the claim that their intrinsic paradigm can imitate the functioning of the human brain. Nevertheless, there are three important issues that must be accounted for the implementation of a neural network based autonomous system performing an automaton human intelligent behavior. The first one is related to the collection of an appropriate database for training and evaluating the system performance. The second issue is the adoption of an appropriate machine representation of the data which implies the selection of suitable data features for the problem at hand. Finally, the choice of the classification scheme can impact on the achieved results. This introductive chapter summarizes the efforts that have been made in the field of neural network models along the above mentioned research directions through the contents of the chapters included in this book.
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Esposito, A., Bassis, S., Morabito, F.C. (2015). Recent Advances of Neural Networks Models and Applications: An Introduction. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-18164-6_1
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DOI: https://doi.org/10.1007/978-3-319-18164-6_1
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