Results 241 to 250 of about 259,517 (255)
Few-shot hotel industry site selection prediction method based on meta learning algorithms and transportation accessibility. [PDF]
Sci RepLi N, Wu H.
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2022
Swarm learning is a kind of decentralized machine learning. In this paper, we propose a new framework of decentralized collaborative learning, called swarm meta learning, by combining swarm learning with meta learning, blockchain, and federated learning.
Tian, Xiao +2 more
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Swarm learning is a kind of decentralized machine learning. In this paper, we propose a new framework of decentralized collaborative learning, called swarm meta learning, by combining swarm learning with meta learning, blockchain, and federated learning.
Tian, Xiao +2 more
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2009
The application of Machine Learning (ML) and Data Mining (DM) tools to classification and regression tasks has become a standard, not only in research but also in administrative agencies, commerce and industry (e.g., finance, medicine, engineering).
Christophe Giraud-Carrier +3 more
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The application of Machine Learning (ML) and Data Mining (DM) tools to classification and regression tasks has become a standard, not only in research but also in administrative agencies, commerce and industry (e.g., finance, medicine, engineering).
Christophe Giraud-Carrier +3 more
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Goal of this work is to make acquaintance and study meta-learningu methods, program algorithm and compare with other machine learning methods.
Hang Wang, Sen Lin, Junshan Zhang
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Hang Wang, Sen Lin, Junshan Zhang
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Algorithm Selection via Meta-Learning and Active Meta-Learning
2019To find most suitable classifier is possible either through cross-validation, which suffers from computational cost or through expert advice which is not always feasible to have. Meta-Learning can be a better approach to automate this process, by generating Meta-Examples which is a combination of performance results of classification algorithms on ...
Nirav Bhatt +3 more
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Meta-learning in Reinforcement Learning
Neural Networks, 2003Meta-parameters in reinforcement learning should be tuned to the environmental dynamics and the animal performance. Here, we propose a biologically plausible meta-reinforcement learning algorithm for tuning these meta-parameters in a dynamic, adaptive manner.
Nicolas, Schweighofer, Kenji, Doya
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Meta-learning in active inference
Behavioral and Brain SciencesAbstract Binz et al. propose meta-learning as a promising avenue for modelling human cognition. They provide an in-depth reflection on the advantages of meta-learning over other computational models of cognition, including a sound discussion on how their proposal can accommodate neuroscientific insights.
O. Penacchio, A. Clemente
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Proceedings of the 2021 5th International Conference on Electronic Information Technology and Computer Engineering, 2021
Jiangbo Liu, Zhenyong Fu
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Jiangbo Liu, Zhenyong Fu
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