In this paper, we develop an efficient logistic regression model for multiple instance learning that combines L1 and L2 regularisation techniques. An L1 regularised logistic regr...
L1 regularization is effective for feature selection, but the resulting optimization is challenging due to the non-differentiability of the 1-norm. In this paper we compare state...
This work deals with the problem to optimise the energy consumption of an embedded system. On system level, tasks are assumed to have a certain CPU-usage they need for completion. ...
Compressed graphs representation has become an attractive research topic because of its applications to the manipulation of huge Web graphs in main memory. By far the best current ...
To support real-time computation with large, possibly evolving point clouds and range data, we fit a trimmed uniform tensor-product spline function from one direction. The graph ...