Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2016; 25th International C Alessandro E.P. Villa,Paolo Masulli,Antonio Javier Confe

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,Wertschöpfungsketten und Geschäftsmodelle,NNs possessing many layers and providing a good internal representation of the learned data. Due to the potentially high complexity of CNNs on the other hand they are prone to overfitting and as a result, regularization techniques are needed to improve the performance and minimize overfitting. Howev

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https://doi.org/10.1007/978-3-531-90097-1ge regarding the rules of chess, a deep neural network is trained using a combination of unsupervised pretraining and supervised training. The unsupervised training extracts high level features from a given position, and the supervised training learns to compare two chess positions and select the mo

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,Wertschöpfungsketten und Geschäftsmodelle,eature maps with increasing specificity and invariance along feedforward paths. The present study explores the possibility of specifically training convolutional networks to resemble the primate cortex more closely. In particular, in addition to supervised learning to minimize an output error functi

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