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GECCO
2004
Springer
135views Optimization» more  GECCO 2004»
13 years 10 months ago
CellNet Co-Ev: Evolving Better Pattern Recognizers Using Competitive Co-evolution
A model for the co-evolution of patterns and classifiers is presented. The CellNet system for generating binary classifiers is used as a base for experimentation. The CellNet syste...
Taras Kowaliw, Nawwaf N. Kharma, Chris Jensen, Hus...
ICDAR
2009
IEEE
13 years 11 months ago
Combination of Measurement-Level Classifiers: Output Normalization by Dynamic Time Warping
Classifier combination is a powerful strategy to support useful solutions in difficult classification problems. Notwithstanding, the effectiveness of a multi-classifier system str...
Giuseppe Pirlo, Donato Impedovo, Claudia Adamita T...
ICML
2006
IEEE
14 years 5 months ago
Nightmare at test time: robust learning by feature deletion
When constructing a classifier from labeled data, it is important not to assign too much weight to any single input feature, in order to increase the robustness of the classifier....
Amir Globerson, Sam T. Roweis
PLDI
2003
ACM
13 years 9 months ago
A provably sound TAL for back-end optimization
Typed assembly languages provide a way to generate machinecheckable safety proofs for machine-language programs. But the soundness proofs of most existing typed assembly languages...
Juan Chen, Dinghao Wu, Andrew W. Appel, Hai Fang
GECCO
2005
Springer
141views Optimization» more  GECCO 2005»
13 years 10 months ago
Constructing good learners using evolved pattern generators
Self-organization of brain areas in animals begins prenatally, evidently driven by spontaneously generated internal patterns. The neural structures continue to develop postnatally...
Vinod K. Valsalam, James A. Bednar, Risto Miikkula...