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Technology Transfer and Learning
Technology Analysis & Strategic Management, 2002Despite the fact that international technology transfer has been widely studied its management still encounters many difficulties. To fully understand the issues that are relevant to the process of transferring production technology, it is necessary to determine the important factors that influence this process.
Steenhuis, Harm-Jan, de Bruijn, Erik J.
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IEEE Transactions on Circuits and Systems for Video Technology, 2020
A major assumption in data mining and machine learning is that the training set and test set come from the same domain. They share the same feature space and have the same distribution. However, in many real-world applications, the training set and test set usually come from different domains.
Zhihao Peng 0002 +5 more
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A major assumption in data mining and machine learning is that the training set and test set come from the same domain. They share the same feature space and have the same distribution. However, in many real-world applications, the training set and test set usually come from different domains.
Zhihao Peng 0002 +5 more
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Proceedings of the AAAI Conference on Artificial Intelligence, 2010
Transfer learning aims at reusing the knowledge in some source tasks to improve the learning of a target task. Many transfer learning methods assume that the source tasks and the target task be related, even though many tasks are not related in reality.
Bin Cao 0001 +4 more
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Transfer learning aims at reusing the knowledge in some source tasks to improve the learning of a target task. Many transfer learning methods assume that the source tasks and the target task be related, even though many tasks are not related in reality.
Bin Cao 0001 +4 more
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Proceedings of the 20th ACM international conference on Information and knowledge management, 2011
Active learning traditionally assumes that labeled and unlabeled samples are subject to the same distributions and the goal of an active learner is to label the most informative unlabeled samples. In reality, situations may exist that we may not have unlabeled samples from the same domain as the labeled samples (i.e.
Zhenfeng Zhu +4 more
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Active learning traditionally assumes that labeled and unlabeled samples are subject to the same distributions and the goal of an active learner is to label the most informative unlabeled samples. In reality, situations may exist that we may not have unlabeled samples from the same domain as the labeled samples (i.e.
Zhenfeng Zhu +4 more
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Interhemispheric transfer of learning
Life Sciences, 1965Abstract Interhemispheric transfer of a simple discrimination was studied by means of spreading cortical depression. Following a single trial with both hemispheres functional transfer to the untrained hemisphere did not occur if SD was elicited in either hemisphere fifteen seconds after the transfer trial.
O S, RAY, G, EMLEY
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Transfer Learning and Ensemble Learning
2020In this chapter, we start from transfer learning and introduce the relationship between different learners; we use ensemble learning to combine them together and hope to get a strong learner from a weak learner by changing the training dataset or adjusting parameters of networks. Our ultimate goal is to implement a robust and stable classifier.
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Boosting for transfer learning
Proceedings of the 24th international conference on Machine learning, 2007Traditional machine learning makes a basic assumption: the training and test data should be under the same distribution. However, in many cases, this identical-distribution assumption does not hold. The assumption might be violated when a task from one new domain comes, while there are only labeled data from a similar old domain.
Wenyuan Dai +3 more
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Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015
Transfer learning, which leverages knowledge from source domains to enhance learning ability in a target domain, has been proven effective in various applications. One major limitation of transfer learning is that the source and target domains should be directly related.
Ben Tan +3 more
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Transfer learning, which leverages knowledge from source domains to enhance learning ability in a target domain, has been proven effective in various applications. One major limitation of transfer learning is that the source and target domains should be directly related.
Ben Tan +3 more
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Learning Transfers via Transfer Learning
2021 IEEE Workshop on Innovating the Network for Data-Intensive Science (INDIS), 2021Md. Arifuzzaman, Engin Arslan
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