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ICDE
2008
IEEE
137views Database» more  ICDE 2008»
16 years 1 months ago
Stop Chasing Trends: Discovering High Order Models in Evolving Data
Abstract-- Many applications are driven by evolving data -patterns in web traffic, program execution traces, network event logs, etc., are often non-stationary. Building prediction...
Shixi Chen, Haixun Wang, Shuigeng Zhou, Philip S. ...
82
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GECCO
2006
Springer
195views Optimization» more  GECCO 2006»
15 years 3 months ago
Studying XCS/BOA learning in Boolean functions: structure encoding and random Boolean functions
Recently, studies with the XCS classifier system on Boolean functions have shown that in certain types of functions simple crossover operators can lead to disruption and, conseque...
Martin V. Butz, Martin Pelikan
ECCV
2010
Springer
14 years 11 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof
CHI
2009
ACM
16 years 6 days ago
Adaptive brain-computer interface
Passive brain-computer interfaces are designed to use brain activity as an additional input, allowing the adaptation of the interface in real time according to the user's men...
Audrey Girouard
ISSTA
2010
ACM
14 years 11 months ago
Automatically identifying critical input regions and code in applications
Applications that process complex inputs often react in different ways to changes in different regions of the input. Small changes to forgiving regions induce correspondingly smal...
Michael Carbin, Martin C. Rinard