We consider the general problem of learning from both labeled and unlabeled data. Given a set of data points, only a few of them are labeled, and the remaining points are unlabele...
Fei Wang, Changshui Zhang, Helen C. Shen, Jingdong...
This work presents a real-time, data-parallel approach for global label assignment on regular grids. The labels are selected according to a Markov random field energy with a Potts...
Christopher Zach, David Gallup, Jan-Michael Frahm,...
A common search problem in the World Wide Web concerns finding information if it is not known when the sources of information appear and how long sources will be available on the ...
Dirk Kukulenz, Nils Hoeller, Sven Groppe, Volker L...
We show how and why using genetic operators that are applied with probabilities that depend on the fitness rank of a genotype or phenotype offers a robust alternative to the Sim...
In this paper we propose a novel approach to content-based image retrieval with relevance feedback, which is based on the random walker algorithm introduced in the context of inte...