Journal ArticleDOI
Learning representations by back-propagating errors
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TLDR
Back-propagation repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector, which helps to represent important features of the task domain.Abstract:
We describe a new learning procedure, back-propagation, for networks of neurone-like units. The procedure repeatedly adjusts the weights of the connections in the network so as to minimize a measure of the difference between the actual output vector of the net and the desired output vector. As a result of the weight adjustments, internal ‘hidden’ units which are not part of the input or output come to represent important features of the task domain, and the regularities in the task are captured by the interactions of these units. The ability to create useful new features distinguishes back-propagation from earlier, simpler methods such as the perceptron-convergence procedure1.read more
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Journal ArticleDOI
Remote sensing of forest change using artificial neural networks
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Journal ArticleDOI
Machine learning methods for better water quality prediction
Ali Najah Ahmed,Faridah Othman,Haitham Abdulmohsin Afan,Rusul Khaleel Ibrahim,Chow Ming Fai,Shabbir Hossain,Mohammad Ehteram,Ahmed El-Shafie +7 more
TL;DR: A Neuro-Fuzzy Inference System (WDT-ANFIS) based augmented wavelet de-noising technique has been recommended that depends on historical data of the water quality parameter and exhibited a significant improvement in predicting accuracy for all theWater quality parameters and outperformed all the recommended models.
Proceedings Article
Neural Trojans
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Book ChapterDOI
Large-scale Multi-label Text Classification - Revisiting Neural Networks
TL;DR: This paper proposed to use a comparably simple NN approach with recently proposed learning techniques for large-scale multi-label text classification tasks, and showed that BP-MLL's ranking loss can be efficiently and effectively replaced with the commonly used cross entropy error function, and demonstrate that several advances in neural network training that have been developed in the realm of deep learning can be effectively employed in this setting.
Journal ArticleDOI
Deep residual learning-based fault diagnosis method for rotating machinery
TL;DR: The results of this study suggest that the proposed intelligent fault diagnosis method for rotating machinery offers a new and promising approach, and significantly improves the information flow throughout the network, which is well suited for processing machinery vibration signal with variable sequential length.