We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
The model-driven software development for hard real-time systems promotes the usage of the platform independent model as major design artifact. It is used to develop the software l...
Predicting the running time of a parallel program is useful for determining the optimal values for the parameters of the implementation and the optimal mapping of data on processo...
A machine-learning and a string-matching approach to automated subject classification of text were compared, as to their performance, advantages and downsides. The former approach ...
We present the Auckland Layout Model (ALM), a constraint-based technique for specifying 2D layout as it is used for arranging the controls in a GUI. Most GUI frameworks offer layo...