We propose a novel reasoning engine for context-aware ubiquitous computing middleware in this paper. Our reasoning engine supports both rulebased reasoning and machine learning rea...
Donghai Guan, Weiwei Yuan, Seong Jin Cho, Andrey G...
We focus on the problem of selecting the few vehicles in a fleet that are expected to last the longest without failure. The prediction of each vehicle’s remaining life is based o...
In this paper we compare performance of several heuristics in generating informative generic/query-oriented extracts for newspaper articles in order to learn how topic prominence ...
— A key step in many statistical learning methods used in machine learning involves solving a convex optimization problem containing one or more hyper-parameters that must be sel...
Kristin P. Bennett, Jing Hu, Xiaoyun Ji, Gautam Ku...
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...