Data augmentation for unbalanced face recognition training sets
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DOI: 10.1016/j.neucom.2016.12.013
Abstract: Face recognition remains a challenging problem. While one-to-one face verification has been largely tackled, verification-based classification problem still demands effort. To further enhance the verification models, one solution is to fully utilize the unbalanced training… read more here.
Keywords: training sets; augmentation; data augmentation; face ... See more keywords
Learning from small datasets containing nominal attributes
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DOI: 10.1016/j.neucom.2018.02.069
Abstract: Abstract In many small-data-learning problems, owing to the incomplete data structure, explicit information for decision makers is limited. Although machine learning algorithms are extensively applied to extract knowledge, most of them are developed without considering… read more here.
Keywords: continuous outputs; learning small; training sets; small datasets ... See more keywords
An Efficient High-Speed Channel Modeling Method Based on Optimized Design-of-Experiment (DoE) for Artificial Neural Network Training
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Transactions on Electromagnetic Compatibility"
DOI: 10.1109/temc.2018.2796091
Abstract: This paper studies the optimized setup in the design-of-experiment (DoE) method to efficiently construct precise artificial neural network (ANN) model for high-speed channel. The accuracy of an ANN model is in general determined by the… read more here.
Keywords: neural network; design experiment; experiment doe; method ... See more keywords