A multi-task multi-class learning method for automatic identification of heavy minerals from river sand

Choosing an appropriate training set size when using existing data to train neural networks for land cover segmentation

“…For multi-class land cover applications (10 classes in our case), converting them to single class models using one-vs-rest or one-vs-one strategy will lead to explosions on numbers of binary models or datasets when the number of classes increases (Li et al 2020;Di et al 2019;Chen et al 2016). Considering these strategies complicates the comparison between large and small training sets.…”

A Review of Artificial Intelligence Technologies in Mineral Identification: Classification and Visualization

“…From the figure, we can clearly see that deep neural network, represented by convolutional neural network, is an important component of this type of recognition method. When performing hierarchical feature extraction of complex structured data, often, not a single algorithm is used, but rather, a learning system with different network architectures and complemented by different optimization strategies [79]. Similarly, this type of identification is also concerned with the extraction of sample data by traditional mineral identification aids, such as infrared devices, remote sensing, etc., to obtain spectroscopy information.…”