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» A Study of Empirical Learning for an Involved Problem
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CORR
2010
Springer
152views Education» more  CORR 2010»
15 years 2 months ago
Neuroevolutionary optimization
Temporal difference methods are theoretically grounded and empirically effective methods for addressing reinforcement learning problems. In most real-world reinforcement learning ...
Eva Volná
HEURISTICS
2002
152views more  HEURISTICS 2002»
15 years 1 months ago
A Constraint-Based Method for Project Scheduling with Time Windows
This paper presents a heuristic algorithm for solving RCPSP/max, the resource constrained project scheduling problem with generalized precedence relations. The algorithm relies, a...
Amedeo Cesta, Angelo Oddi, Stephen F. Smith
JSS
2010
104views more  JSS 2010»
14 years 8 months ago
Using hybrid algorithm for Pareto efficient multi-objective test suite minimisation
Test suite minimisation techniques seek to reduce the effort required for regression testing by selecting a subset of test suites. In previous work, the problem has been considere...
Shin Yoo, Mark Harman
BMCBI
2006
150views more  BMCBI 2006»
15 years 2 months ago
Instance-based concept learning from multiclass DNA microarray data
Background: Various statistical and machine learning methods have been successfully applied to the classification of DNA microarray data. Simple instance-based classifiers such as...
Daniel P. Berrar, Ian Bradbury, Werner Dubitzky
COLT
1999
Springer
15 years 6 months ago
Beating the Hold-Out: Bounds for K-fold and Progressive Cross-Validation
The empirical error on a test set, the hold-out estimate, often is a more reliable estimate of generalization error than the observed error on the training set, the training estim...
Avrim Blum, Adam Kalai, John Langford