Results 231 to 240 of about 295,561 (260)

Granular transfer learning

Neurocomputing
Transfer learning is aimed at supporting the design of machine learning models in the target domain Dt, given that the knowledge (model) has already been constructed in the source domain Ds. The domains Dtand Ds (as well as the corresponding tasks Ts and Tt) are similar, yet not identical.
Al-Hmouz, Rami, Pedrycz, Witold, Awadallah, Medhat, Ammari, Ahmed   +3 more
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Accelerating Active Learning with Transfer Learning

2013 IEEE 13th International Conference on Data Mining, 2013
Active learning, transfer learning, and related techniques are unified by a core theme: efficient and effective use of available data. Active learning offers scalable solutions for building effective supervised learning models while minimizing annotation effort.
David C. Kale, Yan Liu
openaire   +1 more source

An Introduction to Transfer Learning

2008
Many 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This assumption does not hold, however, as in many cases of Web mining and wireless computing when labeled data becomes outdated or test data are from a different domain with training data.
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Learning Transferable Representations

2019
A first contribution of this thesis is to propose causality as a language for problems of distribution shift. First, we consider domain generalisation, where no data from the test distribution are observed during training. What assumptions can be made regarding the relation between train and test distributions for transfer to succeed?
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Semi-supervised Learning with Transfer Learning

2013
Traditional machine learning works well under the assumption that the training data and test data are in the same distribution. However, in many real-world applications, this assumption does not hold. The research of knowledge transfer has received considerable interest recently in Natural Language Processing to improve the domain adaptation of machine
Huiwei Zhou, Yan Zhang, Degen Huang, Lishuang Li   +3 more
openaire   +1 more source

A Comprehensive Survey on Transfer Learning

Proceedings of the IEEE, 2021
Fuzhen Zhuang, Zhiyuan Qi, Dongbo Xi
exaly  

Transfer Learning in Deep Reinforcement Learning: A Survey

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Zhuangdi Zhu, Kaixiang Lin, Anil K Jain   +2 more
exaly  

Transfer Meta Learning

2022 26th International Conference on Pattern Recognition (ICPR), 2022
Nico Zengeler, Tobias Glasmachers, Uwe Handmann   +2 more
openaire   +1 more source

A Review of Deep Transfer Learning and Recent Advancements

Technologies, 2023
Mohammadreza Iman, Hamid R Arabnia
exaly  

A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

Mechanical Systems and Signal Processing, 2022
Weihua Li, Ruyi Huang, Jipu Li
exaly