In binary classification there are two types of errors, and in many applications these may have very different costs. We consider two learning frameworks that address this issue: minimax classification, where we seek to minimize the maximum of the false alarm and miss rates, and Neyman-Pearson (NP) classification, where we seek to minimize the miss ...

In recent years, signal processing has come under mounting pressure to accommodate the increasingly high-dimensional raw data generated by modern sensing systems. Despite extraordinary advances in computational power, processing the signals produced in application areas such as imaging, video, remote surveillance, spectroscopy, and genomic data ...