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Density estimation-based method to determine sample size for random sample partition of big data
Random sample partition (RSP) is a newly developed big data representation and management model to deal with big data approximate computation...
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Small-sample size problems solving based on incremental learning: an adaptive Bayesian quadrature approach
When solving programming problems with objectives, we are often faced with the challenge of insufficient samples. And when new samples are generated,...
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Machine Learning-Based Fingerprinting Positioning in Massive MIMO Networks: Analysis on the Impact of Small Training Sample Size to the Positioning Performance
It is well known that the bigger the training dataset, the higher the performance of deep learning algorithms. But gathering/collecting huge real...
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SSGAN: A Semantic Similarity-Based GAN for Small-Sample Image Augmentation
Image sample augmentation refers to strategies for increasing sample size by modifying current data or synthesizing new data based on existing data....
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Identification Method for Rice Pests with Small Sample Size Problems Combining Deep Learning and Metric Learning
To achieve accurate identification of rice pests with small sample size problems under complex backgrounds, we proposed a rice pest identification...
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DF classification algorithm for constructing a small sample size of data-oriented DF regression model
The deep forest (DF) model is built using a multilayer ensemble of forest units through decision tree aggregation. DF presents characteristics of an...
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EdgeNet: a low-power image recognition model based on small sample information
Existing deep convolutional neural networks that rely on large datasets typically require images with high resolution and deep neural network models...
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Multiscale dilated convolution and swin-transformer for small sample gearbox fault diagnosis
Mechanical equipment usually operates in noisy and variable load environments, which presents serious challenges for existing intelligent diagnostic...
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UAV image object recognition method based on small sample learning
In recent years, unmanned aerial vehicles (UAVs) have developed rapidly. Because of their small size, low cost, and strong maneuverability, they have...
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Lightweight Multiview Mask Contrastive Network for Small-Sample Hyperspectral Image Classification
Deep learning methods have made significant progress in the field of hyperspectral image (HSI) classification. However, these methods often rely on a...
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Constructing small sample datasets with game mixed sampling and improved genetic algorithm
The issue of categorizing imbalanced data is becoming increasingly prevalent. While existing methodologies have demonstrated notable advancements in...
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Data Augmentation Generated by Generative Adversarial Network for Small Sample Datasets Clustering
In the field of data mining, the performance of clustering is largely affected by the number of samples. However, obtaining enough data samples in...
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Random forest kernel for high-dimension low sample size classification
High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning. From medical images to text...
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Sample Size Estimation for Effective Modelling of Classification Problems in Machine Learning
High quality and sufficiently numerous data are fundamental to develo** any machine learning model. In the absence of a prior estimate on the...
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Reducing Overfitting Risk in Small-Sample Learning with ANN: A Case of Predicting Graduate Admission Probability
AI-assisted personal educational career planning holds immense promise, especially with artificial neural network algorithms demonstrating...
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Small-Sample Coal-Rock Recognition Model Based on MFSC and Siamese Neural Network
Given the advantages of deep learning in feature extraction and learning ability, it has been used in coal-rock recognition. Deep learning techniques...
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GAN-SNR-Shrinkage-Based Network for Modulation Recognition with Small Training Sample Size
Modulation recognition plays an important role in non-cooperative communications. In practice, only a small number of samples can be collected for...
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Missing data imputation and classification of small sample missing time series data based on gradient penalized adversarial multi-task learning
In practice, time series data obtained is usually small and missing, which poses a great challenge to data analysis in different domains, such as...
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Bearing fault diagnosis method based on improved Siamese neural network with small sample
Fault diagnosis of rolling bearings is very important for monitoring the health of rotating machinery. However, in actual industrial production,...
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Effective sample size, dimensionality, and generalization in covariate shift adaptation
In supervised learning, training and test datasets are often sampled from distinct distributions. Domain adaptation techniques are thus required....
