Task‐induced brain functional connectivity as a representation of schema for mediating unsupervised and supervised learning dynamics in language acquisition
Sign Up to like & getrecommendations! Published in 2021 at "Brain and Behavior"
DOI: 10.1002/brb3.2157
Abstract: Based on the schema theory advanced by Rumelhart and Norman, we shed light on the individual variability in brain dynamics induced by hybridization of learning methodologies, particularly alternating unsupervised learning and supervised learning in language… read more here.
Keywords: schema; language acquisition; brain; supervised learning ... See more keywords
Comparison of supervised learning statistical methods for classifying commercial beers and identifying patterns
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Chemometrics"
DOI: 10.1002/cem.3216
Abstract: In this study, 13 properties (alcohol‐, real extract‐, flavonoid‐, anthocyanin, glucose, fructose, maltose, sucrose content, EBC [European Brewery Convention] and L*a*b* color, bitterness) of 21 beers (alcohol‐free pale lagers, alcohol‐free beer‐based mixed drinks, beer‐based mixed… read more here.
Keywords: color; supervised learning; error; error rate ... See more keywords
Comparison of supervised-learning approaches for designing a channelized observer for image quality assessment in CT.
Sign Up to like & getrecommendations! Published in 2023 at "Medical physics"
DOI: 10.1002/mp.16227
Abstract: BACKGROUND The current paradigm for evaluating computed tomography (CT) system performance relies on a task-based approach. As the Hotelling observer (HO) provides an upper bound of observer performances in specific signal detection tasks, the literature… read more here.
Keywords: image quality; csvm; supervised learning; quality assessment ... See more keywords
Self-supervised denoising of projection data for low-dose cone-beam CT.
Sign Up to like & getrecommendations! Published in 2023 at "Medical physics"
DOI: 10.1002/mp.16421
Abstract: BACKGROUND Convolutional neural networks (CNNs) have shown promising results in image denoising tasks. While most existing CNN-based methods depend on supervised learning by directly mapping noisy inputs to clean targets, high-quality references are often unavailable for… read more here.
Keywords: projection; supervised learning; cone beam; self supervised ... See more keywords
The maximum points-based supervised learning rule for spiking neural networks
Sign Up to like & getrecommendations! Published in 2019 at "Soft Computing"
DOI: 10.1007/s00500-018-3576-0
Abstract: As the third generation of neural networks, Spiking Neural Networks (SNNs) have made great success in pattern recognition fields. However, the existing training methods for SNNs are not efficient enough because of the temporal encoding… read more here.
Keywords: based supervised; maximum points; neural networks; supervised learning ... See more keywords
Supervised learning based on the self-organizing maps for forward kinematic modeling of Stewart platform
Sign Up to like & getrecommendations! Published in 2017 at "Neural Computing and Applications"
DOI: 10.1007/s00521-017-3095-4
Abstract: In this study, we propose an alternative technique for solving the forward kinematic problem of parallel manipulator which is designed based on generalized Stewart platform. The focus of this work is to predict a pose… read more here.
Keywords: vqtam lle; stewart platform; self organizing; forward kinematic ... See more keywords
A robust weakly supervised learning of deep Conv-Nets for surface defect inspection
Sign Up to like & getrecommendations! Published in 2020 at "Neural Computing and Applications"
DOI: 10.1007/s00521-020-04819-5
Abstract: Automatic defect detection is a challenging task owing to the complex textured background with non-uniform intensity distribution, weak differences between defects and background, diversity of defect types, and high cost of annotated samples. In order… read more here.
Keywords: robust weakly; learning deep; weakly supervised; segmentation ... See more keywords
Electrodiagnostic accuracy in polyneuropathies: supervised learning algorithms as a tool for practitioners
Sign Up to like & getrecommendations! Published in 2020 at "Neurological Sciences"
DOI: 10.1007/s10072-020-04499-y
Abstract: Objective The interpretation of electrophysiological findings may lead to misdiagnosis in polyneuropathies. We investigated the electrodiagnostic accuracy of three supervised learning algorithms (SLAs): shrinkage discriminant analysis, multinomial logistic regression, and support vector machine (SVM), and… read more here.
Keywords: accuracy polyneuropathies; accuracy; learning algorithms; polyneuropathies supervised ... See more keywords
Primitive-contrastive network: data-efficient self-supervised learning from robot demonstration videos
Sign Up to like & getrecommendations! Published in 2021 at "Applied Intelligence"
DOI: 10.1007/s10489-021-02527-8
Abstract: Due to the costly collection of expert demonstrations for robots, robot imitation learning suffers from the demonstration-insufficiency problem. A promising solution to this problem is self-supervised learning that leverages pretext tasks to extract general and… read more here.
Keywords: primitive contrastive; self supervised; supervised learning; contrastive network ... See more keywords
Semi-supervised learning for k-dependence Bayesian classifiers
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DOI: 10.1007/s10489-021-02531-y
Abstract: Bayesian network classifiers (BNCs) are powerful tools for graphically encoding the dependency relationships among variables in a directed acyclic graph and reasoning under conditions of uncertainty. Ever increasing data quantity makes ever more urgent the… read more here.
Keywords: testing instance; learning dependence; semi supervised; dependence bayesian ... See more keywords
Incremental supervised learning: algorithms and applications in pattern recognition
Sign Up to like & getrecommendations! Published in 2019 at "Evolutionary Intelligence"
DOI: 10.1007/s12065-019-00203-y
Abstract: The most effective well-known methods in the context of static machine learning offer no alternative to evolution and dynamic adaptation to integrate new data or to restructure problems already partially learned. In this area, the… read more here.
Keywords: incremental supervised; supervised learning; learning algorithms; pattern recognition ... See more keywords