A survey on epistemic (model) uncertainty in supervised learning: Recent advances and applications
Quantifying the uncertainty of supervised learning models plays an important role in making
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
more reliable predictions. Epistemic uncertainty, which usually is due to insufficient …
Conventional to deep ensemble methods for hyperspectral image classification: A comprehensive survey
Hyperspectral image classification (HSIC) has become a hot research topic. Hyperspectral
imaging (HSI) has been widely used in a wide range of real-world application areas due to …
imaging (HSI) has been widely used in a wide range of real-world application areas due to …
Emotion recognition from multi-channel EEG via deep forest
Recently, deep neural networks (DNNs) have been applied to emotion recognition tasks
based on electroencephalography (EEG), and have achieved better performance than …
based on electroencephalography (EEG), and have achieved better performance than …
Few-shot hyperspectral image classification with self-supervised learning
Recently, few-shot learning (FSL) has been introduced for hyperspectral image (HSI)
classification with few labeled samples. However, existing FSL-based HSI classification …
classification with few labeled samples. However, existing FSL-based HSI classification …
Multi-feature input deep forest for EEG-based emotion recognition
Due to the rapid development of human–computer interaction, affective computing has
attracted more and more attention in recent years. In emotion recognition …
attracted more and more attention in recent years. In emotion recognition …
Spectral–spatial and cascaded multilayer random forests for tree species classification in airborne hyperspectral images
F Tong, Y Zhang - IEEE Transactions on Geoscience and …, 2022 - ieeexplore.ieee.org
The rapid development of remote sensing sensors has made it possible to collect airborne
hyperspectral data with high spectral and spatial resolution. Such data can provide valuable …
hyperspectral data with high spectral and spatial resolution. Such data can provide valuable …
Identification of soybean varieties based on hyperspectral imaging technology and one‐dimensional convolutional neural network
H Li, L Zhang, H Sun, Z Rao, H Ji - Journal of Food Process …, 2021 - Wiley Online Library
Variety identification of seeds is essential to ensure the purity and yield of the variety. A
model based on hyperspectral imaging technology and one‐dimensional convolutional …
model based on hyperspectral imaging technology and one‐dimensional convolutional …
H2A2Net: A Hybrid Convolution and Hybrid Resolution Network with Double Attention for Hyperspectral Image Classification
Deep learning (DL) has recently been a core ingredient in modern computer vision tasks,
triggering a wave of revolutions in various fields. The hyperspectral image (HSI) …
triggering a wave of revolutions in various fields. The hyperspectral image (HSI) …
Feature fusion via deep random forest for facial age estimation
In the last few years, human age estimation from face images attracted the attention of many
researchers in computer vision and machine learning fields. This is due to its numerous …
researchers in computer vision and machine learning fields. This is due to its numerous …
Deep random forest with ferroelectric analog content addressable memory
Deep random forest (DRF), which combines deep learning and random forest, exhibits
comparable accuracy, interpretability, low memory and computational overhead to deep …
comparable accuracy, interpretability, low memory and computational overhead to deep …