Results 141 to 150 of about 2,516,559 (186)
Behavior recognition of cage-free multi-broilers based on spatiotemporal feature learning. [PDF]
Poult SciHu Y +6 more
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MSDAFL: molecular substructure-based dual attention feature learning framework for predicting drug-drug interactions. [PDF]
BioinformaticsHou C, Duan G, Yan C.
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A Joint Network of Edge-Aware and Spectral-Spatial Feature Learning for Hyperspectral Image Classification. [PDF]
Sensors (Basel)Zheng J +6 more
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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
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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
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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
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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
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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
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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
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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
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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
2018
FL is a technique that models the behavior of data from a subset of attributes only. It also shows the correlation between detection performance and traffic model quality efficiently (Palmieri et al., Concurrency Comput Pract Exp 26(5):1113–1129, 2014). However, feature extraction and feature selection are different.
Kwangjo Kim +2 more
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FL is a technique that models the behavior of data from a subset of attributes only. It also shows the correlation between detection performance and traffic model quality efficiently (Palmieri et al., Concurrency Comput Pract Exp 26(5):1113–1129, 2014). However, feature extraction and feature selection are different.
Kwangjo Kim +2 more
openaire +1 more source