Results 11 to 20 of about 116,076 (264)

Enhancing clinically cardiovascular machine learning model for risk prediction via sample augmentation [PDF]

Frontiers in Medicine
BackgroundSmall sample dataset and heterogeneous distributions limit the robustness and implementability of machine learning models for structured clinical data.
Xiaoyu Tang, Xiaoyu Tang, Xiaoyu Tang, Min Tang, Wu Liu, Shaoyang Cui, Shaoyang Cui, Shaoyang Cui   +7 more
doaj   +2 more sources

Increasing prediction accuracy of pathogenic staging by sample augmentation with a GAN. [PDF]

PLoS ONE, 2021
Accurate prediction of cancer stage is important in that it enables more appropriate treatment for patients with cancer. Many measures or methods have been proposed for more accurate prediction of cancer stage, but recently, machine learning, especially ...
ChangHyuk Kwon, Sangjin Park, Soohyun Ko, Jaegyoon Ahn   +3 more
doaj   +2 more sources

Sample design, sample augmentation, and estimation for Wave 2 of the NSHAP. [PDF]

J Gerontol B Psychol Sci Soc Sci, 2014
The sample for the second wave (2010) of National Social Life, Health, and Aging Project (NSHAP) was designed to increase the scientific value of the Wave 1 (2005) data set by revisiting sample members 5 years after their initial interviews and augmenting this sample where possible.There were 2 important innovations.
O'Muircheartaigh C, English N, Pedlow S, Kwok PK.   +3 more
europepmc   +5 more sources

Hyperspectral estimation of leaf chlorophyll under small-sample conditions via spectral augmentation and weighted ensemble learning [PDF]

Frontiers in Plant Science
IntroductionUnder small-sample conditions, hyperspectral leaf chlorophyll estimation is affected by high-dimensional collinearity, measurement noise, and cross-source acquisition discrepancies. Existing studies often treat training-distribution expansion
Jiahui Xu, Jiahui Xu, Jiahui Xu, Yalong Song, Yalong Song, Yalong Song, Yuanyuan Liu, Yuanyuan Liu, Yuanyuan Liu, Shuo Zhang, Shuo Zhang, Shuo Zhang, Guansan Zhu, Guansan Zhu, Guansan Zhu, Wentao Li, Wentao Li, Wentao Li, Xufeng Wang, Xufeng Wang, Xufeng Wang, Jianfei Xing, Jianfei Xing, Jianfei Xing, Jianfei Xing   +24 more
doaj   +2 more sources

Unsupervised Domain Adaptation Algorithm for Time Series Based on Adaptive Contrastive Learning [PDF]

Entropy
Time series data find extensive applications in finance, healthcare, and industrial monitoring domains. However, analytical models targeting such data are subject to notable constraints imposed by the rigid independent and identically distributed (IID ...
Huayong Liu, Peng Lin
doaj   +2 more sources

Online-Dynamic-Clustering-Based Soft Sensor for Industrial Semi-Supervised Data Streams

Sensors, 2023
In the era of big data, industrial process data are often generated rapidly in the form of streams. Thus, how to process such sequential and high-speed stream data in real time and provide critical quality variable predictions has become a critical issue
Yuechen Wang, Huaiping Jin, Xiangguang Chen, Bin Wang, Biao Yang, Bin Qian   +5 more
doaj   +1 more source

ParticleAugment: Sampling-based data augmentation

Computer Vision and Image Understanding, 2023
8 ...
Alexander Tsaregorodtsev, Vasileios Belagiannis   +1 more
openaire   +2 more sources

Semi-supervised Learning Method Based on Automated Mixed Sample Data Augmentation Techniques [PDF]

Jisuanji kexue, 2022
Consistency-based semi-supervised learning methods typically use simple data augmentation methods to achieve consistent predictions for both original inputs and perturbed inputs.The effectiveness of this approach is difficult to be guaranteed when the ...
XU Hua-jie, CHEN Yu, YANG Yang, QIN Yuan-zhuo
doaj   +1 more source

SSS Underwater Target Image Samples Augmentation Based on the Cross-Domain Mapping Relationship of Images of the Same Physical Object

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Side-scan sonar (SSS) image sample augmentation plays an important role in improving the effect of deep-learning-based underwater target detection.
Yulin Tang, Liming Wang, Shaofeng Bian, Shaohua Jin, Yuting Dong, Houpu Li, Bing Ji   +6 more
doaj   +1 more source

Self-supervised Action Recognition Based on Skeleton Data Augmentation and Double Nearest Neighbor Retrieval [PDF]

Jisuanji kexue, 2023
Traditional self-supervised methods based on skeleton data often take different data augmentation of a sample as positive examples,and the rest of the samples are regarded as negative examples,which makes the ratio of positive and negative samples ...
WU Yushan, XU Zengmin, ZHANG Xuelian, WANG Tao
doaj   +1 more source