Articles

In recent years, vector-based machine learning algorithms, such as random forests, support vector machines, and 1-D convolutional neural networks, have shown... 
Support vector machines | recurrent neural network (RNN) | deep learning | Recurrent neural networks | gated recurrent unit (GRU) | hyperspectral image classification | Logic gates | long short-term memory (LSTM) | Data models | Convolutional neural network (CNN) | Hyperspectral imaging | Deep learning | Gated recurrent unit (GRU) | Long short-term memory (LSTM) | Recurrent neural network (RNN) | Hyperspectral image classification | LSTM | IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY | SPECTRAL-SPATIAL CLASSIFICATION | ENGINEERING, ELECTRICAL & ELECTRONIC | GEOCHEMISTRY & GEOPHYSICS | REMOTE SENSING | FRAMEWORK | Neural networks | Usage | Machine learning | Forests | Divergence | Methodology | Pixels | Risks | Training | Classification | Computer architecture | Sequencing | Framework | Activation analysis | Data analysis | Aversion learning | Parameters | Categories | Data processing | Learning algorithms | Satellites | Algorithms | Serial learning | Reasoning | Image classification