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SENSYS
2010
ACM
13 years 6 months ago
The Jigsaw continuous sensing engine for mobile phone applications
Supporting continuous sensing applications on mobile phones is challenging because of the resource demands of long-term sensing, inference and communication algorithms. We present...
Hong Lu, Jun Yang, Zhigang Liu, Nicholas D. Lane, ...
GECCO
2008
Springer
184views Optimization» more  GECCO 2008»
13 years 9 months ago
Analysis of mammography reports using maximum variation sampling
A genetic algorithm (GA) was developed to implement a maximum variation sampling technique to derive a subset of data from a large dataset of unstructured mammography reports. It ...
Robert M. Patton, Barbara G. Beckerman, Thomas E. ...
SIGIR
2006
ACM
14 years 2 months ago
Large scale semi-supervised linear SVMs
Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...
Vikas Sindhwani, S. Sathiya Keerthi
GECCO
2007
Springer
138views Optimization» more  GECCO 2007»
14 years 2 months ago
Bayesian estimation of rule accuracy in UCS
Learning Classifier Systems differ from many other classification techniques, in that new rules are constantly discovered and evaluated. This feature of LCS gives rise to an im...
James A. R. Marshall, Gavin Brown, Tim Kovacs
ECML
2004
Springer
14 years 7 days ago
Naive Bayesian Classifiers for Ranking
It is well-known that naive Bayes performs surprisingly well in classification, but its probability estimation is poor. In many applications, however, a ranking based on class prob...
Harry Zhang, Jiang Su