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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»
14 years 12 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»
14 years 11 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 6 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»
14 years 12 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 4 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