Most existing methods of semi-supervised clustering introduce supervision from outside, e.g., manually label some data samples or introduce constrains into clustering results. Thi...
Given a task T , a pool of individuals X with different skills, and a social network G that captures the compatibility among these individuals, we study the problem of finding X ,...
In this paper we describe a software pipelining framework, CALiBeR (Cluster Aware Load Balancing Retiming Algorithm), suitable for compilers targeting clustered embedded VLIW proc...
Semi-supervised learning methods construct classifiers using both labeled and unlabeled training data samples. While unlabeled data samples can help to improve the accuracy of trai...
Text clustering is most commonly treated as a fully automated task without user supervision. However, we can improve clustering performance using supervision in the form of pairwi...