Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis
Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis is a scholarly work, published in 2018 in ''Natural and Engineering Sciences''. The main subjects of the publication include electroencephalography, kernel, deep learning, artificial intelligence, neurofeedback, artificial neural network, feature, extreme learning machine, speech recognition, autoencoder, brain–computer interface, machine learning, pattern recognition, computer science, and generative model. The optimization of the predefined classification parameters for the supervised models eases reaching the global optimality with exact zero training error.