Results 21 to 30 of about 1,961,679 (175)
Frontiers in Psychology, 2017 The current experiment investigated generalizability of motor learning in proximal versus distal effectors in upper extremities. Twenty-eight participants were divided into three groups: training proximal effectors, training distal effectors, and no ...
Tore K. Aune +3 more
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Measuring learning transfer in a financial institution (Part 2)
SA Journal of Industrial Psychology, 2006 The purpose of this study was to identify learning transfer variables impacting on learning transfer using the Learning Transfer System Inventory (LTSI).
W J Coetsee, R Eiselen
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Deep Transfer Learning for Biology Cross-Domain Image Classification
Journal of Control Science and Engineering, 2021 Automatic biology image classification is essential for biodiversity conservation and ecological study. Recently, due to the record-shattering performance, deep convolutional neural networks (DCNNs) have been used more often in biology image ...
Chunfeng Guo, Bin Wei, Kun Yu
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On spatial selectivity and prediction across conditions with fMRI [PDF]
, 2012 Researchers in functional neuroimaging mostly use activation coordinates to formulate their hypotheses. Instead, we propose to use the full statistical images to define regions of interest (ROIs).
Schwartz, Yannick +2 more
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Improvement of Heterogeneous Transfer Learning Efficiency by Using Hebbian Learning Principle
Applied Sciences, 2020 Transfer learning algorithms have been widely studied for machine learning in recent times. In particular, in image recognition and classification tasks, transfer learning has shown significant benefits, and is getting plenty of attention in the research
Arjun Magotra, Juntae Kim
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Frontiers in Earth Science, 2023 The main purpose of this study is to analyze the main influencing factors of the landslide in the coal mine area and, on this basis, establish the sensitivity zoning model of the landslide.
Yongguo Zhang +3 more
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Stochastic Ensemble Policy Transfer [PDF]
Jisuanji kexue yu tansuo, 2022 Reinforcement learning (RL) has achieved great success on sequential decision-making problems. Along with the fast advances of RL, transfer learning (TL) arises as an important technique to accelerate the learning process of RL by leveraging and ...
CHANG Tian, ZHANG Zongzhang, YU Yang
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Effects of Force Level and Hand Dominance on Bilateral Transfer of a Fine Motor Skill [PDF]
, 2016 Our research is about bilateral transfer, a concept in motor learning where skills learned by one limb are "transferred", allowing the opposite limb to benefit from what was learned by the first limb.
Muller, Karl, Rai, Aakarsh
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Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2019 In machine learning, transfer learning is concerned with utilising prior knowledge as a way to improve the process of training a new model in a different, but related, domain. Transfer learning has been shown to be beneficial across a large set of problems.
Brandon Muller +3 more
openaire +2 more sources
Lautum Regularization for Semi-supervised Transfer Learning [PDF]
, 2020 Transfer learning is a very important tool in deep learning as it allows propagating information from one "source dataset" to another "target dataset", especially in the case of a small number of training examples in the latter.
Giryes, Raja +2 more
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