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» Evolving Fuzzy-Rule-Based Classifiers From Data Streams
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EUROGP
2007
Springer
126views Optimization» more  EUROGP 2007»
13 years 11 months ago
Training Binary GP Classifiers Efficiently: A Pareto-coevolutionary Approach
The conversion and extension of the Incremental Pareto-Coevolution Archive algorithm (IPCA) into the domain of Genetic Programming classification is presented. In particular, the ...
Michal Lemczyk, Malcolm I. Heywood
VLDB
2005
ACM
140views Database» more  VLDB 2005»
14 years 26 days ago
Loadstar: Load Shedding in Data Stream Mining
In this demo, we show that intelligent load shedding is essential in achieving optimum results in mining data streams under various resource constraints. The Loadstar system intro...
Yun Chi, Haixun Wang, Philip S. Yu
CVPR
2004
IEEE
14 years 9 months ago
An Unsupervised, Online Learning Framework for Moving Object Detection
Object detection with a learned classifier has been applied successfully to difficult tasks such as detecting faces and pedestrians. Systems using this approach usually learn the ...
Vinod Nair, James J. Clark
ASC
2008
13 years 7 months ago
Info-fuzzy algorithms for mining dynamic data streams
Most data mining algorithms assume static behavior of the incoming data. In the real world, the situation is different and most continuously collected data streams are generated by...
Lior Cohen, Gil Avrahami, Mark Last, Abraham Kande...

Book
361views
15 years 4 months ago
Introduction to Statistical Signal Processing
"A random or stochastic process is a mathematical model for a phenomenon that evolves in time in an unpredictable manner from the viewpoint of the observer. The phenomenon m...
R.M. Gray