New PDF release: Adaptive and Natural Computing Algorithms: 11th

By Toshinori Deguchi, Junya Fukuta, Naohiro Ishii (auth.), Marco Tomassini, Alberto Antonioni, Fabio Daolio, Pierre Buesser (eds.)

ISBN-10: 3642372120

ISBN-13: 9783642372124

ISBN-10: 3642372139

ISBN-13: 9783642372131

The e-book constitutes the refereed court cases of the eleventh foreign convention on Adaptive and usual Computing Algorithms, ICANNGA 2013, held in Lausanne, Switzerland, in April 2013.
The fifty one revised complete papers provided have been conscientiously reviewed and chosen from a complete of ninety one submissions. The papers are equipped in topical sections on neural networks, evolutionary computation, gentle computing, bioinformatics and computational biology, complicated computing, and applications.

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Read Online or Download Adaptive and Natural Computing Algorithms: 11th International Conference, ICANNGA 2013, Lausanne, Switzerland, April 4-6, 2013. Proceedings PDF

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Extra resources for Adaptive and Natural Computing Algorithms: 11th International Conference, ICANNGA 2013, Lausanne, Switzerland, April 4-6, 2013. Proceedings

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In this paper, we propose a novel asymmetric coefficient designed for measuring the hierarchy-caused degree of asymmetry in time series datasets. The coefficient will then be used in order to weight the standard Euclidean distance, and subsequently, in order to obtain an asymmetric similarity utilized in the asymmetric SOM. The proposed coefficient is formulated this way that it finds hierarchical associations in time series dataset, even if there are no identical series. S. Stock Market Dataset verify and confirm the effectiveness of the proposed approach.

Only few lower bounds on the approximation error by one-hidden-layer networks are known. Moreover, such bounds are mostly non constructive and hold for types of computational units that are not commonly used [11, 12]. , [13, 14]). Such networks have been called deep networks, in contrast to shallow ones that have merely one hidden layer [13]. As training deep networks involves complicated nonlinear optimization procedures, generally it is more difficult than training shallow ones. Thus it is desirable to develop some theoretical foundations for the characterization of tasks that require considerably larger model complexity and/or size of weights when computed by shallow networks than by deep ones.

LNCS (LNAI), vol. 3214, pp. 124–130. Springer, Heidelberg (2004) 12. : Neural Computations by Asymmetric Networks with Nonlinearities. , Ribeiro, B. ) ICANNGA 2007, Part II. LNCS, vol. 4432, pp. 37–45. uk Dept. edu Abstract. Learning in the brain requires complementary mechanisms: potentiation and activity-dependent homeostatic scaling. We introduce synaptic scaling to a biologically-realistic spiking model of neocortex which can learn changes in oscillatory rhythms using STDP, and show that scaling is necessary to balance both positive and negative changes in input from potentiation and atrophy.

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Adaptive and Natural Computing Algorithms: 11th International Conference, ICANNGA 2013, Lausanne, Switzerland, April 4-6, 2013. Proceedings by Toshinori Deguchi, Junya Fukuta, Naohiro Ishii (auth.), Marco Tomassini, Alberto Antonioni, Fabio Daolio, Pierre Buesser (eds.)


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