We introduce unFEAR, Unsupervised Feature Extraction Clustering, to identify economic crisis regimes. Given labeled crisis and non-crisis episodes and the corresponding features values, unFEAR uses ...

Most clustering techniques depend on a distance measure which means the source data must be strictly numeric. A related, but also little-explored, technique for anomaly detection is to create an ...

In order to evaluate a dataset of over 11 million cells from a study of dengue fever, Yale researchers developed a cutting-edge neural network that recognizes and represents patterns in large datasets ...

The autoencoder network model for HIV classification, proposed in this paper, thus outperforms the conventional feedforward neural network models and is a much better classifier.