We prove the strongest known bound for the risk of hypotheses selected from the ensemble generated by running a learning algorithm incrementally on the training data. Our result i...
The Semantic Web envisions a World Wide Web in which data is described with rich semantics and applications can pose complex queries. To this point, researchers have defined new l...
Alon Y. Halevy, Zachary G. Ives, Peter Mork, Igor ...
We address the e-rulemaking problem of reducing the manual labor required to analyze public comment sets. In current and previous work, for example, text categorization techniques...
We propose an unconventional but highly effective approach
to robust fitting of multiple structures by using statistical
learning concepts. We design a novel Mercer kernel
for t...
We propose methods for outlier handling and noise reduction using weighted local linear smoothing for a set of noisy points sampled from a nonlinear manifold. The methods can be u...
Jin Hyeong Park, Zhenyue Zhang, Hongyuan Zha, Rang...