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Reject Inference, Augmentation and Sample Selection [PDF]

European Journal of Operational Research, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Banasik, John, Crook, Jonathan
core   +4 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

Sampling constrained probability distributions using Spherical Augmentation [PDF]

, 2015
Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit, many copula models, and latent Dirichlet allocation (LDA).
A Beskos, A Beskos, A Gelfand, A Pakman, AE Brockwell, B Leimkuhler, B Shahbaba, C Andrieu, C Hans, C Sherlock, CJ Geyer, D Randal, DM Blei, GH Golub, GO Roberts, GO Roberts, GO Roberts, I Murray, IE Frank, M Girolami, M Spivak, M West, MD Hoffman, N Chopin, NS Pillai, O Cappé, P Mykland, P Neal, P Neal, R Douc, R Tibshirani, RM Neal, RM Neal, RV Craiu, S Amari, S Byrne, S Duane, S Lan, SL Cotter, T Park, WR Gilks, Y Ahmadian   +41 more
core   +3 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

Sample Mixed-Based Data Augmentation for Domestic Audio Tagging [PDF]

, 2018
Audio tagging has attracted increasing attention since last decade and has various potential applications in many fields. The objective of audio tagging is to predict the labels of an audio clip.
Kong, Qiuqiang, Liao, Feifan, Wang, Dezhi, Wang, Huaimin, Wei, Shengyun, Xu, Kele   +5 more
core   +4 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

ParticleAugment: Sampling-based data augmentation

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

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

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

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