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An Introduction to Transfer Learning
2008Many existing data mining and machine learning techniques are based on the assumption that training and test data fit the same distribution. This assumption does not hold, however, as in many cases of Web mining and wireless computing when labeled data becomes outdated or test data are from a different domain with training data.
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2022 26th International Conference on Pattern Recognition (ICPR), 2022
Nico Zengeler +2 more
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Nico Zengeler +2 more
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A Comprehensive Survey on Transfer Learning
Proceedings of the IEEE, 2021Dongbo Xi, Fuzhen Zhuang, Zhiyuan Qi
exaly
Transfer Learning in Deep Reinforcement Learning: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Anil Jain, Zhuangdi Zhu, Kaixiang Lin
exaly
A Review of Deep Transfer Learning and Recent Advancements
Technologies, 2023Hamid R Arabnia, Mohammadreza Iman
exaly
2013
The use of machine learning to predict output from data, using a model, is a well studied area. There are, however, a number of real-world applications that require a model to be produced but have little or no data available of the specific environment. These situations are prominent in Intelligent Environments (IEs).
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The use of machine learning to predict output from data, using a model, is a well studied area. There are, however, a number of real-world applications that require a model to be produced but have little or no data available of the specific environment. These situations are prominent in Intelligent Environments (IEs).
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A review on transfer learning in EEG signal analysis
Neurocomputing, 2021Mengjie Huang, Rui Yang, Nianyin Zeng
exaly
Trustworthy Transfer Learning: Transferability and Trustworthiness
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023Jun Wu 0019, Jingrui He
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Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016
IEEE Transactions on Instrumentation and Measurement, 2023Chen Xiaohan +2 more
exaly