In this paper we propose a novel general framework for unsupervised model adaptation. Our method is based on entropy which has been used previously as a regularizer in semi-superv...
Ariya Rastrow, Frederick Jelinek, Abhinav Sethy, B...
We combine linear discriminant analysis (LDA) and K-means clustering into a coherent framework to adaptively select the most discriminative subspace. We use K-means clustering to ...
We present in this paper an approach aiming at adapting software components. It focuses on adapting component structures instead of adapting component services. Among the motivati...
Flooding protocols for wireless networks in general have been shown to be very inefficient and therefore are mainly used in network initialization or route discovery and maintenan...
The core of most registration algorithms aligns scan data by pairs, minimizing their relative distance. This local optimization must generally pass through a validation procedure t...