Compressed Sensing is a new paradigm for acquiring the compressible signals that arise in many applications. These signals can be approximated using an amount of information much ...
Anna C. Gilbert, Martin J. Strauss, Joel A. Tropp,...
In this paper, we present a new incremental learning strategy for handwritten character recognition systems. This learning strategy enables the recognition system to learn “rapi...
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. ...