We present a general boosting method extending functional gradient boosting to optimize complex loss functions that are encountered in many machine learning problems. Our approach...
Generalization bounds depending on the margin of a classifier are a relatively recent development. They provide an explanation of the performance of state-of-the-art learning syste...
The mean of a data set is one trivial representation of data from one class. Recently, mutual interdependence analysis (MIA) has been successfully used to extract more involved re...
Many current medical image analysis problems involve learning thousands or even millions of model parameters from extremely few samples. Employing sparse models provides an effecti...
In previous work, we developed the Illum-PF-MT, which is the PFMT idea applied to the problem of tracking temporally and spatially varying illumination change. In many practical p...