By E. J. Palomo, E. Domínguez, R. M. Luque, J. Muñoz (auth.), Joan Cabestany, Ignacio Rojas, Gonzalo Joya (eds.)
This two-volume set LNCS 6691 and 6692 constitutes the refereed court cases of the eleventh foreign Work-Conference on synthetic Neural Networks, IWANN 2011, held in Torremolinos-Málaga, Spain, in June 2011. The 154 revised papers have been rigorously reviewed and chosen from 202 submissions for presentation in volumes. the second one quantity contains seventy six papers prepared in topical sections on video and photograph processing; hybrid man made neural networks: types, algorithms and knowledge; advances in computing device studying for bioinformatics and computational biomedicine; biometric platforms for human-machine interplay; information mining in biomedicine; bio-inspired combinatorial optimization; utilizing evolutionary computation and nature-inspired algorithms to formal tools; fresh advances on fuzzy common sense and gentle computing purposes; new advances in conception and purposes of ICA-based algorithms; organic and bio-inspired dynamical platforms; and interactive and cognitive environments. The final part comprises nine papers from the foreign Workshop on clever structures for Context-Based details Fusion, ISCIF 2011, held at IWANN 2011.
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Additional resources for Advances in Computational Intelligence: 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Torremolinos-Málaga, Spain, June 8-10, 2011, Proceedings, Part II
J. Palomo, E. M. Luque, and J. I. es Abstract. A new approach for image compression based on the GHSOM model has been proposed in this paper. The SOM has some problems related to its ﬁxed topology and its lack of representation of hierarchical relations among input data. The GHSOM solves these limitations by generating a hierarchical architecture that is automatically determined according to the input data and reﬂects the inherent hierarchical relations among them.
That is, mj controls the effect of the j-th coordinate of the input samples on the energy function E which the proposed competitive neural network tries to minimize. Hence, the selection of m made by the genetic algorithm deeply influences the behavior of the competitive neural network.
Manuel Mart´ın-Merino 89 A Novel Heuristic for Building Reduced-Set SVMs Using the Self-Organizing Map . . . . . . . . . . . . . . . . . . . . . . . Ajalmar R. Rocha Neto and Guilherme A. Barreto 97 An Additive Decision Rules Classiﬁer for Network Intrusion Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . Tommaso Pani and Francisco de Toro 105 Multi-modal Opponent Behaviour Prognosis in E-Negotiations .