A generative model may overlook underrepresented modes that are less frequent in the empirical data distribution.
However, under certain circumstances, the training of GANs can lead to mode collapse or mode dropping, i. e. the generative models not being able to sample from the entire probability distribution.
In this way, we provide a purely data-driven way to assess different underlying dynamics of input/output signal pairs, without the need for any system identification step.
The first class of methods employs a distance measure on time series (e. g. Euclidean, Dynamic Time Warping) and a clustering technique (e. g. k-means, k-medoids, hierarchical clustering) to find natural groups in the dataset.
We present a novel risk profiling approach based exclusively on health expenditure data that is available to Belgian mutual health insurers.
We provide theoretical bounds on the quality of our estimates, illustrate the importance of estimating the fraction of positives in the unlabeled set and demonstrate empirically that we are able to reliably estimate ROC and PR curves on real data.
EnsembleSVM is a free software package containing efficient routines to perform ensemble learning with support vector machine (SVM) base models.
The included benchmark comprises three settings with increasing label noise: (i) fully supervised, (ii) PU learning and (iii) PU learning with false positives.