Ordinal regression has become an effective way of learning user preferences, but most of research only focuses on single regression problem. In this paper we introduce collaborati...
Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Krieg...
We introduce dynamic correlated topic models (DCTM) for analyzing discrete data over time. This model is inspired by the hierarchical Gaussian process latent variable models (GP-L...
This study proposes a novel forecasting approach – an adaptive smoothing neural network (ASNN) – to predict foreign exchange rates. In this new model, adaptive smoothing techni...
The objectives of the research project described in this paper are (1) to develop reliable and valid measures for the components of online store image, and (2) to examine the infl...
Can we predict locations of future refactoring based on the development history? In an empirical study of open source projects we found that attributes of software evolution data ...
Jacek Ratzinger, Thomas Sigmund, Peter Vorburger, ...