Journal ArticleDOI
Generalized Hidden-Mapping Transductive Transfer Learning for Recognition of Epileptic Electroencephalogram Signals
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TLDR
The generalized hidden-mapping transductive learning method is proposed to realize transfer learning for several classical intelligent models, including feedforward neural networks, fuzzy systems, and kernelized linear models, which can be trained effectively even though the data available are insufficient for model training.Citations
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Making Sense of Spatio-Temporal Preserving Representations for EEG-Based Human Intention Recognition
TL;DR: Two deep learning-based frameworks with novel spatio-temporal preserving representations of raw EEG streams to precisely identify human intentions are introduced with high accuracy and outperform a set of state-of-the-art and baseline models.
Journal ArticleDOI
A review on transfer learning in EEG signal analysis
TL;DR: Four main methods of transfer learning are described and their practical applications in EEG signal analysis in recent years are explored.
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A sparse stacked denoising autoencoder with optimized transfer learning applied to the fault diagnosis of rolling bearings
TL;DR: The results for data from the Case Western Reserve University Bearing Data Center show that the proposed SSDAE-TL algorithm is feasible and easy to implement for the fault diagnosis of bearings.
Journal ArticleDOI
Unsupervised transfer learning for anomaly detection: Application to complementary operating condition transfer
Gabriel Michau,Olga Fink +1 more
TL;DR: The proposed end-to-end framework uses adversarial deep learning to ensure alignment of the different units' distributions and introduces a new loss, inspired by a dimensionality reduction tool, to enforce the conservation of the inherent variability of each dataset.
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Error Correction Regression Framework for Enhancing the Decoding Accuracies of Ear-EEG Brain–Computer Interfaces
No-Sang Kwak,Seong-Whan Lee +1 more
TL;DR: It is demonstrated that SSVEP BCI based on ear-EEG can achieve reliable performance with the proposed error correction regression (ECR) framework.
References
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Journal ArticleDOI
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
Journal ArticleDOI
A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
Journal ArticleDOI
Multilayer feedforward networks are universal approximators
HornikK.,StinchcombeM.,WhiteH. +2 more
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Ridge regression: biased estimation for nonorthogonal problems
TL;DR: In this paper, an estimation procedure based on adding small positive quantities to the diagonal of X′X was proposed, which is a method for showing in two dimensions the effects of nonorthogonality.
Journal ArticleDOI
A learning algorithm for continually running fully recurrent neural networks
Ronald J. Williams,David Zipser +1 more
TL;DR: The exact form of a gradient-following learning algorithm for completely recurrent networks running in continually sampled time is derived and used as the basis for practical algorithms for temporal supervised learning tasks.