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KDD
2009
ACM
227views Data Mining» more  KDD 2009»
16 years 7 months ago
Efficiently learning the accuracy of labeling sources for selective sampling
Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources (`oracles...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
STOC
2001
ACM
163views Algorithms» more  STOC 2001»
16 years 7 months ago
Extractor codes
We study error-correcting codes for highly noisy channels. For example, every received signal in the channel may originate from some half of the symbols in the alphabet. Our main c...
Amnon Ta-Shma, David Zuckerman
SIGMOD
2004
ACM
144views Database» more  SIGMOD 2004»
16 years 6 months ago
Diamond in the Rough: Finding Hierarchical Heavy Hitters in Multi-Dimensional Data
Data items archived in data warehouses or those that arrive online as streams typically have attributes which take values from multiple hierarchies (e.g., time and geographic loca...
Graham Cormode, Flip Korn, S. Muthukrishnan, Dives...
188
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POPL
2010
ACM
16 years 4 months ago
Contracts Made Manifest
Since Findler and Felleisen [2002] introduced higher-order contracts, many variants have been proposed. Broadly, these fall into two groups: some follow Findler and Felleisen in u...
Benjamin C. Pierce, Michael Greenberg, Stephanie W...
EVOW
2010
Springer
16 years 1 months ago
Parameter Tuning of Evolutionary Algorithms: Generalist vs. Specialist
Abstract. Finding appropriate parameter values for Evolutionary Algorithms (EAs) is one of the persistent challenges of Evolutionary Computing. In recent publications we showed how...
Selmar K. Smit, A. E. Eiben