In this paper, an adaptive semi-supervised rotation forest (SSRoF) algorithm is proposed for
the classification of hyperspectral images with limited training data. Our proposition is based …

For classification tasks, several strategies aim to tackle the problem of not having sufficient
labeled data, usually by automatic labeling or by fully passing this task to a user. Automatic …

Soft sensors are often used to obtain variables that are difficult to directly measure in an
industrial process. In this article, a co-training-based semisupervised soft sensor model is …

Dimensionality reduction techniques, also called projections, are one of the main tools for
visualizing high-dimensional data. To compare such techniques, several quality metrics …

The absence of large annotated datasets to train deep neural networks (DNNs) is an issue
since manual annotation is time-consuming, expensive, and error-prone. Semi-supervised …

Semi-supervised learning has achieved extraordinary success in prevalent image-
classification benchmarks. However, a class-balanced distribution that differs notably from …

Expert human supervision of the large labeled training sets needed by convolutional neural
networks is expensive. To obtain sufficient labeled samples to train a model, one can …

Training deep neural networks is challenging when large and annotated datasets are
unavailable. Extensive manual annotation of data samples is time-consuming, expensive …

This paper firstly introduces the background of the research on neural network and anomaly
identification screening and mineralization prediction under semisupervised learning, then …

Convolution neural networks usually require large labeled data-sets to construct accurate
models. However, in many real-world scenarios, such as global illumination, labeling data …