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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书目名称Artificial Neural Networks and Machine Learning – ICANN 2016影响因子(影响力)




书目名称Artificial Neural Networks and Machine Learning – ICANN 2016影响因子(影响力)学科排名




书目名称Artificial Neural Networks and Machine Learning – ICANN 2016网络公开度




书目名称Artificial Neural Networks and Machine Learning – ICANN 2016网络公开度学科排名




书目名称Artificial Neural Networks and Machine Learning – ICANN 2016被引频次




书目名称Artificial Neural Networks and Machine Learning – ICANN 2016被引频次学科排名




书目名称Artificial Neural Networks and Machine Learning – ICANN 2016年度引用




书目名称Artificial Neural Networks and Machine Learning – ICANN 2016年度引用学科排名




书目名称Artificial Neural Networks and Machine Learning – ICANN 2016读者反馈




书目名称Artificial Neural Networks and Machine Learning – ICANN 2016读者反馈学科排名

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模仿

未开化

Integration of Unsupervised and Supervised Criteria for Deep Neural Networks Trainingg encourage the incorporation of this idea into on-line learning approaches. The interest of this method in time-series forecasting is motivated by the study of predictive models for domotic houses with intelligent control systems.

Anticlimax

The Effects of Regularization on Learning Facial Expressions with Convolutional Neural NetworksN, almost halving its validation error. A visualization technique is applied to the CNNs to highlight their activations for different inputs, illustrating a significant difference between a standard CNN and a regularized CNN.

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https://doi.org/10.1007/978-3-322-95653-8grating spatial and temporal tactile sensor data from a piezo-resistive sensor array through deep learning techniques, the network is not only able to classify the contact state into stable versus slipping, but also to distinguish between rotational and translation slippage. We evaluated different n

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https://doi.org/10.1007/978-3-658-05036-8sults for a deeply-trained model for emotion recognition through the use of facial expression images. We explore two Convolutional Neural Network (CNN) architectures that offer automatic feature extraction and representation, followed by fully connected softmax layers to classify images into seven e