We present an improved bound on the difference between training and test errors for voting classifiers. This improved averaging bound provides a theoretical justification for popu...
Kernel learning plays an important role in many machine learning tasks. However, algorithms for learning a kernel matrix often scale poorly, with running times that are cubic in t...
This paper proposes a novel approach to the visualization of complex, but interrelated, sets of information to ease user cognition. Principally, it explores the potential of provi...
Structured data including sets, sequences, trees and graphs, pose significant challenges to fundamental aspects of data management such as efficient storage, indexing, and simila...
Xiaohong Wang, Aaron M. Smalter, Jun Huan, Gerald ...
We describe our participation in the Link-the-Wiki track at INEX 2009. We apply machine learning methods to the anchor-to-best-entry-point task and explore the impact of the follow...