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» Evaluating algorithms that learn from data streams
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GECCO
2005
Springer
134views Optimization» more  GECCO 2005»
16 years 10 days ago
Predicting mining activity with parallel genetic algorithms
We explore several different techniques in our quest to improve the overall model performance of a genetic algorithm calibrated probabilistic cellular automata. We use the Kappa ...
Sam Talaie, Ryan E. Leigh, Sushil J. Louis, Gary L...
BIOSYSTEMS
2007
72views more  BIOSYSTEMS 2007»
15 years 7 months ago
On the use of multi-objective evolutionary algorithms for survival analysis
This paper proposes and evaluates a multi-objective evolutionary algorithm for survival analysis. One aim of survival analysis is the extraction of models from data that approxima...
Christian Setzkorn, Azzam Fouad George Taktak, Ber...
BMCBI
2008
153views more  BMCBI 2008»
15 years 7 months ago
2DB: a Proteomics database for storage, analysis, presentation, and retrieval of information from mass spectrometric experiments
Background: The amount of information stemming from proteomics experiments involving (multi dimensional) separation techniques, mass spectrometric analysis, and computational anal...
Jens Allmer, Sebastian Kuhlgert, Michael Hippler
ICML
2008
IEEE
16 years 7 months ago
Memory bounded inference in topic models
What type of algorithms and statistical techniques support learning from very large datasets over long stretches of time? We address this question through a memory bounded version...
Ryan Gomes, Max Welling, Pietro Perona
GECCO
2005
Springer
129views Optimization» more  GECCO 2005»
16 years 10 days ago
Post-processing clustering to reduce XCS variability
XCS is a stochastic algorithm, so it does not guarantee to produce the same results when run with the same input. When interpretability matters, obtaining a single, stable result ...
Flavio Baronti, Alessandro Passaro, Antonina Stari...