Latent Hinge-Minimax Risk Minimization for Inference from a Small Number of Training Samples

Deep Learning (DL) methods show very good performance when trained on large, balanced data sets. However, many practical problems involve imbalanced data sets, or/and classes with a small number of training samples. The performance of DL methods as well as more traditional classifiers drops significantly in such settings. Most of the existing solutions for… CONTINUE READING