Results 11 to 20 of about 3,973,078 (288)
Transfer Learning in Hierarchical Feature Spaces [PDF]
2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE), 2015 Transfer learning provides an approach to solve target tasks more quickly and effectively by using previously acquired knowledge learned from source tasks. As one category of transfer learning approaches, feature-based transfer learning approaches aim to find a latent feature space shared between source and target domains.
Hua Zuo +4 more
openaire +3 more sources
Transfer Learning Based Data Feature Transfer for Fault Diagnosis [PDF]
IEEE Access, 2020 The development of sensor technology provides massive data for data-driven fault diagnosis. In recent years, more and more scholars are studying artificial intelligence technology to solve the bottleneck in fault diagnosis.
Wei Xu, Yi Wan, Tian-Yu Zuo, Xin-Mei Sha
doaj +3 more sources
Comparing Transfer Learning to Feature Optimization in Microstructure Classification [PDF]
, 2021 Human analysis of research data is slow and inefficient. In recent years machine learning tools have advanced our capability to perform tasks normally carried out by humans, such as image segmentation and classification.
Taylor D., Sparks, Debanshu, Banerjee
core +1 more source
Feature-Supervised Action Modality Transfer [PDF]
2020 25th International Conference on Pattern Recognition (ICPR), 2021 This paper strives for action recognition and detection in video modalities like RGB, depth maps or 3D-skeleton sequences when only limited modality-specific labeled examples are available. For the RGB, and derived optical-flow, modality many large-scale labeled datasets have been made available.
Thoker, F.M., Snoek, C.G.M.
openaire +6 more sources
Transferable Deep Features for Keyword Spotting [PDF]
International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), 2018 Publication in the conference proceedings of IWCIM, Kos island, Greece ...
George Retsinas +2 more
openaire +2 more sources
Feature space transformation for transfer learning [PDF]
The 2012 International Joint Conference on Neural Networks (IJCNN), 2012 In this paper, we propose a study on the use of weighted topological learning and matrix factorization methods to transform the representation space of a sparse dataset in order to increase the quality of learning, and adapt it to the case of transfer learning.
Nistor Grozavu +2 more
openaire +2 more sources
Understanding How Feature Structure Transfers in Transfer Learning [PDF]
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017 Transfer learning transfers knowledge across domains to improve the learning performance. Since feature structures generally represent the common knowledge across different domains, they can be transferred successfully even though the labeling functions across domains differ arbitrarily.
Tongliang Liu +2 more
openaire +1 more source
Web Map / Feature Services Cookbook
, 2023 Learn how to use web map and feature services to easily and quickly provide spatial context, without the need to download and process GBs of data!If you use this cookbook, please cite it as ...
Huang, Andrew +1 more
core +1 more source
An incremental approach to MSE-based feature selection [PDF]
, 2007 Feature selection plays an important role in classification systems. Using classifier error rate as the evaluation function, feature selection is integrated with incremental training.
Guan, SU, Bao, C, Qi, Y
core +6 more sources
Feature Selection for Transfer Learning [PDF]
, 2011 Common assumption in most machine learning algorithms is that, labeled (source) data and unlabeled (target) data are sampled from the same distribution. However, many real world tasks violate this assumption: in temporal domains, feature distributions may vary over time, clinical studies may have sampling bias, or sometimes sufficient labeled data for ...
Selen Uguroglu, Jaime G. Carbonell
openaire +2 more sources