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ESEM
2007
ACM
13 years 11 months ago
Comparison of Outlier Detection Methods in Fault-proneness Models
In this paper, we experimentally evaluated the effect of outlier detection methods to improve the prediction performance of fault-proneness models. Detected outliers were removed ...
Shinsuke Matsumoto, Yasutaka Kamei, Akito Monden, ...
GECCO
2000
Springer
13 years 11 months ago
Modeling GA Performance for Control Parameter Optimization
Optimization of the control parameters of genetic algorithms is often a time consuming and tedious task. In this work we take the meta-level genetic algorithm approach to control ...
Vincent A. Cicirello, Stephen F. Smith
DIS
2008
Springer
13 years 9 months ago
Active Learning for High Throughput Screening
Abstract. An important task in many scientific and engineering disciplines is to set up experiments with the goal of finding the best instances (substances, compositions, designs) ...
Kurt De Grave, Jan Ramon, Luc De Raedt
GRAPHICSINTERFACE
2008
13 years 9 months ago
A model of non-preferred hand mode switching
Effective mode-switching techniques provide users of tablet interfaces with access to a rich set of behaviors. While many researchers have studied the relative performance of mode...
Jaime Ruiz, Andrea Bunt, Edward Lank
AAAI
1997
13 years 9 months ago
Worst-Case Absolute Loss Bounds for Linear Learning Algorithms
The absolute loss is the absolute difference between the desired and predicted outcome. I demonstrateworst-case upper bounds on the absolute loss for the perceptron algorithm and ...
Tom Bylander