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COLT
2004
Springer
15 years 11 months ago
Boosting Based on a Smooth Margin
Abstract. We study two boosting algorithms, Coordinate Ascent Boosting and Approximate Coordinate Ascent Boosting, which are explicitly designed to produce maximum margins. To deri...
Cynthia Rudin, Robert E. Schapire, Ingrid Daubechi...
COLT
2004
Springer
15 years 11 months ago
Statistical Properties of Kernel Principal Component Analysis
The main goal of this paper is to prove inequalities on the reconstruction error for Kernel Principal Component Analysis. With respect to previous work on this topic, our contribu...
Laurent Zwald, Olivier Bousquet, Gilles Blanchard
CP
2004
Springer
15 years 11 months ago
Understanding Random SAT: Beyond the Clauses-to-Variables Ratio
It is well known that the ratio of the number of clauses to the number of variables in a random k-SAT instance is highly correlated with the instance’s empirical hardness. We con...
Eugene Nudelman, Kevin Leyton-Brown, Holger H. Hoo...
EUROGP
2004
Springer
133views Optimization» more  EUROGP 2004»
15 years 11 months ago
Lymphoma Cancer Classification Using Genetic Programming with SNR Features
Lymphoma cancer classification with DNA microarray data is one of important problems in bioinformatics. Many machine learning techniques have been applied to the problem and produc...
Jin-Hyuk Hong, Sung-Bae Cho
GECCO
2004
Springer
144views Optimization» more  GECCO 2004»
15 years 11 months ago
Feature Subset Selection, Class Separability, and Genetic Algorithms
Abstract. The performance of classification algorithms in machine learning is affected by the features used to describe the labeled examples presented to the inducers. Therefore,...
Erick Cantú-Paz
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