Results 271 to 280 of about 510,572 (297)
NGCN: Drug-target interaction prediction by integrating information and feature learning from heterogeneous network. [PDF]
J Cell Mol MedCao J +6 more
europepmc +1 more source
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Learning With Selected Features
IEEE Transactions on Cybernetics, 2022The coming big data era brings data of unprecedented size and launches an innovation of learning algorithms in statistical and machine-learning communities. The classical kernel-based regularized least-squares (RLS) algorithm is excluded in the innovation, due to its computational and storage bottlenecks.
Shao-Bo Lin, Jian Fang, Xiangyu Chang
openaire +2 more sources
Proceedings of the 29th ACM International Conference on Multimedia, 2021
Deep learning-based models have achieved unprecedented performance in single image super-resolution (SISR). However, existing deep learning-based models usually require high computational complexity to generate high-quality images, which limits their applications in edge devices, e.g., mobile phones. To address this issue, we propose a dynamic, channel-
Jun Xiao +4 more
openaire +1 more source
Deep learning-based models have achieved unprecedented performance in single image super-resolution (SISR). However, existing deep learning-based models usually require high computational complexity to generate high-quality images, which limits their applications in edge devices, e.g., mobile phones. To address this issue, we propose a dynamic, channel-
Jun Xiao +4 more
openaire +1 more source
Learning Features for Tracking
2007 IEEE Conference on Computer Vision and Pattern Recognition, 2007We treat tracking as a matching problem of detected key-points between successive frames. The novelty of this paper is to learn classifier-based keypoint descriptions allowing to incorporate background information. Contrary to existing approaches, we are able to start tracking of the object from scratch requiring no off-line training phase before ...
Grabner, Helmut +2 more
openaire +2 more sources
Learning Feature-Sparse Principal Subspace
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023The principal subspace estimation is directly connected to dimension reduction and is important when there is more than one principal component of interest. In this article, we introduce two new algorithms to solve the feature-sparsity constrained PCA problem (FSPCA) for the principal subspace estimation task, which performs feature selection and PCA ...
Feiping Nie +3 more
openaire +2 more sources
Learning feature characteristics
Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), 2002This paper describes a statistical framework for the unsupervised learning of linear filter combinations for feature characterisation. The learning strategy is two step. In the first instance, the EM algorithm is used to learn the foreground probability distribution. This is an abductive process, since we have a detailed model of the background process
S.J. Hickinbotham +2 more
openaire +1 more source
2020
The traditional linear feature extraction methods focus l2, 1on data global structure information or data local structure information. Although these learning methods perform well in some real applications to some extent, they still have some limitations.
Haitao Zhao +3 more
openaire +1 more source
The traditional linear feature extraction methods focus l2, 1on data global structure information or data local structure information. Although these learning methods perform well in some real applications to some extent, they still have some limitations.
Haitao Zhao +3 more
openaire +1 more source