Artificial Neural Networks - ICANN 2007 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I /

This two volume set LNCS 4668 and LNCS 4669 constitutes the refereed proceedings of the 17th International Conference on Artificial Neural Networks, ICANN 2007, held in Porto, Portugal, in September 2007. The 197 revised full papers presented were carefully reviewed and selected from 376 submissions...

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Corporate Author: SpringerLink (Online service)
Other Authors: Marques de Sá, Joaquim. (Editor, http://id.loc.gov/vocabulary/relators/edt), Alexandre, Luis A. (Editor, http://id.loc.gov/vocabulary/relators/edt), Duch, Wlodzislaw. (Editor, http://id.loc.gov/vocabulary/relators/edt), Mandic, Danilo. (Editor, http://id.loc.gov/vocabulary/relators/edt)
Language:English
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2007.
Edition:1st ed. 2007.
Series:Theoretical Computer Science and General Issues ; 4668
Subjects:
Online Access:https://doi.org/10.1007/978-3-540-74690-4
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505 0 |a Learning Theory -- Advances in Neural Network Learning Methods -- Ensemble Learning -- Spiking Neural Networks -- Advances in Neural Network Architectures -- Neural Dynamics and Complex Systems -- Data Analysis -- Estimation -- Spatial and Spatio-Temporal Learning -- Evolutionary Computing -- Meta Learning, Agents Learning -- Complex-Valued Neural Networks (Special Session) -- Temporal Synchronization and Nonlinear Dynamics in Neural Networks (Special Session). 
520 |a This two volume set LNCS 4668 and LNCS 4669 constitutes the refereed proceedings of the 17th International Conference on Artificial Neural Networks, ICANN 2007, held in Porto, Portugal, in September 2007. The 197 revised full papers presented were carefully reviewed and selected from 376 submissions. The 98 papers of the first volume are organized in topical sections on learning theory, advances in neural network learning methods, ensemble learning, spiking neural networks, advances in neural network architectures neural network technologies, neural dynamics and complex systems, data analysis, estimation, spatial and spatio-temporal learning, evolutionary computing, meta learning, agents learning, complex-valued neural networks, as well as temporal synchronization and nonlinear dynamics in neural networks. 
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