Titlebook: Handbook on Neural Information Processing; Monica Bianchini,Marco Maggini,Lakhmi C. Jain Book 20131st edition Springer-Verlag Berlin Heide

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Recurrent Neural Networks,rceptrons trained with gradient descent algorithms (back-propagation) in particular. A recurrent neural network (RNN) is an artificial neural network with internal loops. These internal loops induce recursive dynamics in the networks and thus introduce delayed activation dependencies across the proc

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Supervised Neural Network Models for Processing Graphs,t in which the agent is currently operating. These measurements compose the environment representation that is processed by the agent’s decision algorithms and, hence, they should provide sufficient information to yield the correct actions to support the agent’s life. In general, the developed envir

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Statistical Relational Learning, a complex structure) or external (relationships between this instance and other data elements). Statistical relational learning refers to the use of statistical learning methods in a relational learning context, and the challenges involved in that. In this chapter we give an overview of statistical

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Multiple Classifier Systems: Theory, Applications and Tools,ibution cannot be simply modeled..This affects the results at each stage of the recognition system so that, once this has been designed, its performance cannot be improved over a certain bound, despite the efforts in refining either the classification or the description method.

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Self Organisation and Modal Learning: Algorithms and Applications,ork structure. Modes, in this context, are learning methods that are transferable from one learning architecture to another, such as weight update equations. In modal learning two or more modes may proceed in parallel in different parts of the neural computing structure (layers and neurons), or they

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