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Found 3 papers, 1 papers with code
Variational Autoencoder Kernel Interpretation and Selection for Classification
This work proposed kernel selection approaches for probabilistic classifiers based on features produced by the convolutional encoder of a variational autoencoder.
ProBoost: a Boosting Method for Probabilistic Classifiers
ProBoost, a new boosting algorithm for probabilistic classifiers, is proposed in this work.
Multiple Time Series Fusion Based on LSTM An Application to CAP A Phase Classification Using EEG
The proposed approach is still in the upper range of the best state of the art works despite a difficult dataset, and has the advantage of providing a fully automatic analysis without requiring any manual procedure.
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