We describe a novel approach to obtaining type-accurate information for garbage collection in a hardware and language independent way. Our approach uses a run-time analysis to pro...
Our aim is to develop new database technologies for the approximate matching of unstructured string data using indexes. We explore the potential of the suffix tree data structure i...
In this paper we propose a Neural Net-PMRS hybrid for forecasting time-series data. The neural network model uses the traditional MLP architecture and backpropagation method of tr...
We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical re...
Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, ...
Test collections are the primary drivers of progress in information retrieval. They provide a yardstick for assessing the effectiveness of ranking functions in an automatic, rapi...
Nima Asadi, Donald Metzler, Tamer Elsayed, Jimmy L...