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KCAP
2011
ACM
14 years 9 months ago
Eliciting hierarchical structures from enumerative structures for ontology learning
Some discourse structures such as enumerative structures have typographical, punctuational and laying out characteristics which (1) make them easily identifiable and (2) convey hi...
Mouna Kamel, Bernard Rothenburger
ICDM
2003
IEEE
220views Data Mining» more  ICDM 2003»
16 years 2 days ago
Exploiting Unlabeled Data for Improving Accuracy of Predictive Data Mining
Predictive data mining typically relies on labeled data without exploiting a much larger amount of available unlabeled data. The goal of this paper is to show that using unlabeled...
Kang Peng, Slobodan Vucetic, Bo Han, Hongbo Xie, Z...
181
Voted
SC
2009
ACM
16 years 1 months ago
Lessons learned from a year's worth of benchmarks of large data clouds
In this paper, we discuss some of the lessons that we have learned working with the Hadoop and Sector/Sphere systems. Both of these systems are cloud-based systems designed to sup...
Yunhong Gu, Robert L. Grossman
ICSE
2005
IEEE-ACM
16 years 9 days ago
Observations and lessons learned from automated testing
This report addresses some of our observations made in a dozen of projects in the area of software testing, and more specifically, in automated testing. It documents, analyzes and...
Stefan Berner, Roland Weber, Rudolf K. Keller
PAKDD
2010
ACM
212views Data Mining» more  PAKDD 2010»
15 years 11 months ago
Fast Perceptron Decision Tree Learning from Evolving Data Streams
Abstract. Mining of data streams must balance three evaluation dimensions: accuracy, time and memory. Excellent accuracy on data streams has been obtained with Naive Bayes Hoeffdi...
Albert Bifet, Geoffrey Holmes, Bernhard Pfahringer...