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» Empirical Bernstein stopping
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ICML
2009
IEEE
14 years 8 months ago
Boosting with structural sparsity
Despite popular belief, boosting algorithms and related coordinate descent methods are prone to overfitting. We derive modifications to AdaBoost and related gradient-based coordin...
John Duchi, Yoram Singer
ICML
1999
IEEE
14 years 8 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
14 years 1 months ago
Three interconnected parameters for genetic algorithms
When an optimization problem is encoded using genetic algorithms, one must address issues of population size, crossover and mutation operators and probabilities, stopping criteria...
Pedro A. Diaz-Gomez, Dean F. Hougen
ICSM
2002
IEEE
14 years 9 days ago
A Technique for Dynamic Updating of Java Software
During maintenance, systems are updated to correct faults, improve functionality, and adapt the software to changes in its execution environment. The typical softwareupdate proces...
Alessandro Orso, Anup Rao, Mary Jean Harrold
ICCS
2001
Springer
13 years 11 months ago
Statistical Models for Automatic Performance Tuning
Achieving peak performance from library subroutines usually requires extensive, machine-dependent tuning by hand. Automatic tuning systems have emerged in response, and they typic...
Rich Vuduc, James Demmel, Jeff Bilmes