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Titlebook: Artificial Neural Networks; Methods and Applicat Petia Koprinkova-Hristova,Valeri Mladenov,Nikola K Conference proceedings 2015 Springer In

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Feministische Methodologien und Methoden dynamics describing memory recall in the brain. To demonstrate the effectiveness of the proposed method, local detection of communities in synthetic benchmark networks and real social networks is examined. The community structure detected by our method is highly consistent with the correct community structure of these networks.
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https://doi.org/10.1057/9780230592247ucture is based on one-class classification paradigm. Modified SVM-base outlier detection method is verified in comparison with several classifiers, including the traditional one-class SVM. This algorithm has been tested on real flight data from the Western European and Russia. The test results are presented in the final part of the article.
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Local Detection of Communities by Attractor Neural-Network Dynamics, dynamics describing memory recall in the brain. To demonstrate the effectiveness of the proposed method, local detection of communities in synthetic benchmark networks and real social networks is examined. The community structure detected by our method is highly consistent with the correct community structure of these networks.
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https://doi.org/10.1007/978-3-322-80404-4amework follow similar patterns of characterization. They suggest that some intrinsic computational capabilities of the brain might lie beyond the scope of Turing-equivalentmodels of computation, hence surpass the potentialities every current standard artificial models of computation.
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https://doi.org/10.1007/978-3-663-10056-0tecture can learn the meaning of utterances with respect to visual perception and that it can produce verbal utterances that correctly describe previously unknown scenes. In addition we rigorously study the timescale mechanism (also known as hysteresis) and explore the impact of the architectural connectivity in the language acquisition task.
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