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024 7 _a10.1007/978-3-642-15819-3
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245 1 0 _aArtificial Neural Networks - ICANN 2010
_h[electronic resource] :
_b20th International Conference, Thessaloniki, Greece, September 15-18, 2010, Proceedings, Part I /
_cedited by Konstantinos Diamantaras, Wlodek Duch, Lazaros S. Iliadis.
250 _a1st ed. 2010.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2010.
300 _aXXXI, 587 p. 227 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
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338 _aonline resource
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490 1 _aTheoretical Computer Science and General Issues,
_x2512-2029 ;
_v6352
505 0 _aANN Applications -- Bayesian ANN -- Bio Inspired - Spiking ANN -- Biomedical ANN -- Computational Neuroscience -- Feature Selection/Parameter Identification and Dimensionality Reduction -- Filtering -- Genetic - Evolutionary Algorithms -- Image - Video and Audio Processing.
520 _ath This volume is part of the three-volume proceedings of the 20 International Conference on Arti?cial Neural Networks (ICANN 2010) that was held in Th- saloniki, Greece during September 15-18, 2010. ICANN is an annual meeting sponsored by the European Neural Network Society (ENNS) in cooperation with the International Neural Network So- ety (INNS) and the Japanese Neural Network Society (JNNS). This series of conferences has been held annually since 1991 in Europe, covering the ?eld of neurocomputing, learning systems and other related areas. As in the past 19 events, ICANN 2010 provided a distinguished, lively and interdisciplinary discussion forum for researches and scientists from around the globe. Ito?eredagoodchanceto discussthe latestadvancesofresearchandalso all the developments and applications in the area of Arti?cial Neural Networks (ANNs). ANNs provide an information processing structure inspired by biolo- cal nervous systems and they consist of a large number of highly interconnected processing elements (neurons). Each neuron is a simple processor with a limited computing capacity typically restricted to a rule for combining input signals (utilizing an activation function) in order to calculate the output one. Output signalsmaybesenttootherunitsalongconnectionsknownasweightsthatexcite or inhibit the signal being communicated. ANNs have the ability "to learn" by example (a large volume of cases) through several iterations without requiring a priori ?xed knowledge of the relationships between process parameters.
650 0 _aArtificial intelligence.
_93407
650 0 _aComputer science.
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650 0 _aAlgorithms.
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650 0 _aPattern recognition systems.
_93953
650 0 _aApplication software.
_9144493
650 0 _aComputer vision.
_9144494
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aTheory of Computation.
_9144495
650 2 4 _aAlgorithms.
_93390
650 2 4 _aAutomated Pattern Recognition.
_931568
650 2 4 _aComputer and Information Systems Applications.
_9144496
650 2 4 _aComputer Vision.
_9144497
700 1 _aDiamantaras, Konstantinos.
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700 1 _aDuch, Wlodek.
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700 1 _aIliadis, Lazaros S.
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776 0 8 _iPrinted edition:
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776 0 8 _iPrinted edition:
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830 0 _aTheoretical Computer Science and General Issues,
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