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 overseas Work-Conference on man made 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 geared up in topical sections on video and snapshot processing; hybrid synthetic neural networks: versions, algorithms and information; advances in computing device studying for bioinformatics and computational biomedicine; biometric platforms for human-machine interplay; info mining in biomedicine; bio-inspired combinatorial optimization; utilizing evolutionary computation and nature-inspired algorithms to formal equipment; fresh advances on fuzzy common sense and delicate computing functions; new advances in idea and purposes of ICA-based algorithms; organic and bio-inspired dynamical structures; and interactive and cognitive environments. The final part includes nine papers from the overseas Workshop on clever structures for Context-Based info Fusion, ISCIF 2011, held at IWANN 2011.
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Extra info 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
This technique has been widely used as parameter selection and tuning in combination with neural networks . Although this can be a time-consuming technique, the GA is applied only at the first frames of the sequence, with the aim of getting a weighting mask for the input features of the GCNN approach. This reduces resources and time complexity, getting better tracking rates in the following frames of the sequence. A simple encoding scheme to represent the search space as much as possible was employed, in which the chromosome is a double vector whose length is determined by the number of features extracted.
Each training step, the counter value is decreased by one and, if the value reaches zero then the corresponding neuron is removed. Every time a neuron wins a competition its counter value is changed to the initial value. Therefore, only neurons associated to objects which are not longer in the scene are destroyed, since it is very unlikely for these neurons to win a competition. 3 Feature Weighting Mechanism The use of a set of features to represent an object can help to reinforce and improve the object tracking.
C. Hinterm¨ uller, C. Guger, and G. Edlinger 337 345 353 362 370 378 386 Optimization Algorithms in Graphic Processing Units Variable Selection in a GPU Cluster Using Delta Test . . . . . . . . A. Guill´en, M. van Heeswijk, D. G. J. Herrera, H. Pomares, and I. Rojas 393 XXXVI Table of Contents – Part I Towards ParadisEO-MO-GPU: A Framework for GPU-Based Local Search Metaheuristics . . . . . . . . . . . . . . . . . . . . . . N. -V. Luong, K.