Results 11 to 20 of about 485,564 (300)
The Transmission of Substrate Features
Journal of Modern Languages, 2020 Theories of the formation of creole syntax have been proposed to explain whether substrates and superstrates influence the resultant creole structures, and if so, what the mechanisms are by which they influence them.
Robert Laub
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Comparing Transfer Learning to Feature Optimization in Microstructure Classification [PDF]
, 2021 Human analysis of research data is slow and inefficient. In recent years machine learning tools have advanced our capability to perform tasks normally carried out by humans, such as image segmentation and classification.
Taylor D., Sparks, Debanshu, Banerjee
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Transferable Deep Features for Keyword Spotting [PDF]
International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), 2018 Publication in the conference proceedings of IWCIM, Kos island, Greece ...
George Retsinas +2 more
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Machines, 2022 Fault diagnosis of industrial bearings plays an invaluable role in the health monitoring of rotating machinery. In practice, there is far more normal data than faulty data, so the data usually exhibit a highly skewed class distribution.
Chuanzhu Hao, Junrong Du, Haoran Liang
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Person Re-identification Combined with Clothing Information Transfer
Jisuanji kexue yu tansuo, 2021 Person re-identification (Re-ID) is an identification method based on the overall characteristics of human body, which is usually used to judge whether there is a specific person in the image or video sequence.
YUAN Chunmiao, NIU Ying, GUO Tao, LI Xin
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Feature space transformation for transfer learning [PDF]
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012 In this paper, we propose a study on the use of weighted topological learning and matrix factorization methods to transform the representation space of a sparse dataset in order to increase the quality of learning, and adapt it to the case of transfer learning.
Nistor Grozavu +2 more
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IEEE Access, 2020 Machine learning methods have made great development in data-driven fault diagnosis of rolling bearings. But the intelligent fault diagnosis of intershaft bearing faces the following two dilemmas: 1) the fault vibration is extremely weak, and it is ...
Ya He +3 more
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Fuzzy Inference and Manifold Regularization Combined Feature Transfer Learning
Jisuanji kexue yu tansuo, 2020 Transfer learning leverages the rich data in the source domain to provide support for building accurate models in the target domain. Feature transfer learning is a kind of widely studied technology in transfer learning, but the existing feature transfer ...
SONG Yixuan, DENG Zhaohong, QIN Bin
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Feature Selection for Transfer Learning [PDF]
, 2011 Common assumption in most machine learning algorithms is that, labeled (source) data and unlabeled (target) data are sampled from the same distribution. However, many real world tasks violate this assumption: in temporal domains, feature distributions may vary over time, clinical studies may have sampling bias, or sometimes sufficient labeled data for ...
Selen Uguroglu, Jaime G. Carbonell
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Transfer Learning for Feature Dimensionality Reduction
The International Arab Journal of Information Technology, 2022 Transfer learning is a machine learning methodology by which a model developed for achieving a task is exploited for another related job. Many pre-trained image classification models trained on ImageNet are used for transfer learning. These pre-trained networks could also be used for classifying out of domain images by retraining them.
Nikhila Thribhuvan, Sudheep Elayidom
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