Class-instance label propagation algorithms have been successfully used to fuse information from multiple sources in order to enrich a set of unlabeled instances with class labels...
Zornitsa Kozareva, Konstantin Voevodski, Shang-Hua...
Supervised learning techniques for text classi cation often require a large number of labeled examples to learn accurately. One way to reduce the amountoflabeled datarequired is t...
We present the Feasible Label Routing (FLR) protocol for mobile ad hoc networks, which uses path information to establish routes to destinations on demand. FLR enables loopfree in...
Learning application-specific distance metrics from labeled data is critical for both statistical classification and information retrieval. Most of the earlier work in this area h...
The goal of semi-supervised learning (SSL) methods is to reduce the amount of labeled training data required by learning from both labeled and unlabeled instances. Macskassy and Pr...