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 +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 +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 +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 +5 more
core +4 more sources
Unsupervised Domain Adaptation Algorithm for Time Series Based on Adaptive Contrastive Learning [PDF]
EntropyTime 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
Dimensionality-modulated generative AI for safe biomedical dataset augmentation [PDF]
iScienceSummary: Generative AI can expand small biomedical datasets but may amplify noise and distort statistical relationships. We developed genESOM, a framework integrating an error control system into a generative AI method based on emergent self-organizing ...
Jörn Lötsch +2 more
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Augmenting small tabular health data for training prognostic ensemble machine learning models using generative models [PDF]
BMC Medical Informatics and Decision MakingBackground Small datasets are common in health research. However, the generalization performance of machine learning models is suboptimal when the training datasets are small.
Dan Liu +8 more
doaj +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
ParticleAugment: Sampling-based data augmentation
Computer Vision and Image Understanding, 2023 8 ...
Tsaregorodtsev, Alexander +1 more
openaire +2 more sources