Learning from small datasets containing nominal attributes
Sign Up to like & getrecommendations! Published in 2018 at "Neurocomputing"
DOI: 10.1016/j.neucom.2018.02.069
Abstract: Abstract In many small-data-learning problems, owing to the incomplete data structure, explicit information for decision makers is limited. Although machine learning algorithms are extensively applied to extract knowledge, most of them are developed without considering… read more here.
Keywords: continuous outputs; learning small; training sets; small datasets ... See more keywords
Graph-based vision transformer with sparsity for training on small datasets from scratch
Sign Up to like & getrecommendations! Published in 2025 at "Scientific Reports"
DOI: 10.1038/s41598-025-10408-0
Abstract: Vision Transformers (ViTs) have achieved impressive results in large-scale image classification. However, when training from scratch on small datasets, there is still a significant performance gap between ViTs and Convolutional Neural Networks (CNNs), which is… read more here.
Keywords: small datasets; based vision; attention; graph based ... See more keywords
Uncertainty evaluations from small datasets
Sign Up to like & getrecommendations! Published in 2020 at "Metrologia"
DOI: 10.1088/1681-7575/abd372
Abstract: Small datasets comprising observations made under conditions of repeatability or of reproducibility pervade the practice of measurement science. Many laboratories typically will make only one determination, occasionally they will make two, and only rarely will… read more here.
Keywords: uncertainty; evaluations small; measurement; small datasets ... See more keywords
Image Classification with Small Datasets: Overview and Benchmark
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3172939
Abstract: Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and… read more here.
Keywords: classification small; image classification; small datasets; benchmark ... See more keywords
Transformers Meet Small Datasets
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Access"
DOI: 10.1109/access.2022.3221138
Abstract: The research and application areas of transformers have been extensively enlarged due to the success of vision transformers (ViTs). However, due to the lack of local content acquisition capabilities, the pure transformer architectures cannot be… read more here.
Keywords: network; attention; transformers meet; block ... See more keywords
Fault and Severity Diagnosis Using Deep Learning for Self-Organizing Networks With Imbalanced and Small Datasets
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Access"
DOI: 10.1109/access.2025.3537659
Abstract: With the growing complexity of wireless networks, manual management of networks becomes infeasible. To address this, self-organizing networks (SONs) have been introduced to provide solutions by offering self-organizing approaches to networks. Developing effective self-organizing approaches… read more here.
Keywords: diagnosis; imbalanced small; small datasets; severity ... See more keywords
Improved Multiclassification of Schizophrenia Based on Xgboost and Information Fusion for Small Datasets
Sign Up to like & getrecommendations! Published in 2022 at "Computational and Mathematical Methods in Medicine"
DOI: 10.1155/2022/1581958
Abstract: To improve the performance in multiclass classification for small datasets, a new approach for schizophrenic classification is proposed in the present study. Firstly, the Xgboost classifier is introduced to discriminate the two subtypes of schizophrenia… read more here.
Keywords: fusion; based xgboost; classification; improved multiclassification ... See more keywords
The pertinent single-attribute-based classifier for small datasets classification
Sign Up to like & getrecommendations! Published in 2020 at "International Journal of Electrical and Computer Engineering"
DOI: 10.11591/ijece.v10i3.pp3227-3234
Abstract: Classifying a dataset using machine learning algorithms can be a big challenge when the target is a small dataset. The OneR classifier can be used for such cases due to its simplicity and efficiency. In… read more here.
Keywords: classifier small; single attribute; attribute; pertinent single ... See more keywords
Hierarchical Machine Learning Model for Mechanical Property Predictions of Polyurethane Elastomers From Small Datasets
Sign Up to like & getrecommendations! Published in 2019 at "Frontiers in Materials"
DOI: 10.3389/fmats.2019.00087
Abstract: Polyurethanes are a broad class of material that finds application in coatings, foams, and solid elastomers. The urethane chemistry allows a diversity of monomers to be used, and prediction of mechanical properties, which are determined… read more here.
Keywords: machine learning; chemistry; hierarchical machine; small datasets ... See more keywords
Formation Energy Prediction of Doped Perovskite Structures Based on Transfer Learning with Small Datasets
Sign Up to like & getrecommendations! Published in 2025 at "Crystals"
DOI: 10.3390/cryst15121008
Abstract: Doped perovskites are widely studied in the domain of perovskite material design. However, due to the limited data available for the target materials, machine learning methods based on small datasets become particularly important. In this… read more here.
Keywords: perovskite material; transfer learning; small datasets; based transfer ... See more keywords
Detecting Proximal Caries on Periapical Radiographs Using Convolutional Neural Networks with Different Training Strategies on Small Datasets
Sign Up to like & getrecommendations! Published in 2022 at "Diagnostics"
DOI: 10.3390/diagnostics12051047
Abstract: The present study aimed to evaluate the performance of convolutional neural networks (CNNs) that were trained with small datasets using different strategies in the detection of proximal caries at different levels of severity on periapical… read more here.
Keywords: periapical radiographs; detecting proximal; proximal caries; convolutional neural ... See more keywords