TY  - BOOK
AU  - 
AU  - 
AU  - 
AU  - 
ED  - SpringerLink (Online service)
TI  - Artificial Neural Networks  ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I 
T2  - Lecture Notes in Computer Science,
SN  - 9783540746904
AV  - QA75.5-76.95 
U1  - 004.0151 23
PY  - 2007///
CY  - Berlin, Heidelberg
PB  - Springer Berlin Heidelberg
KW  - Computer science
KW  - Neurosciences
KW  - Database management
KW  - Information systems
KW  - Artificial intelligence
KW  - Optical pattern recognition
KW  - Computer Science
KW  - Computation by Abstract Devices
KW  - Artificial Intelligence (incl. Robotics)
KW  - Pattern Recognition
KW  - Information Systems Applications (incl.Internet)
KW  - Database Management
N1  - 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); ZDB-2-SCS; ZDB-2-LNC
N2  - 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
UR  - http://dx.doi.org/10.1007/978-3-540-74690-4
ER  -