Neural networks and the bias/variance dilemma
Neural Computation
Filter likelihoods and exhaustive learning
Proceedings of the workshop on Computational learning theory and natural learning systems (vol. 2) : intersections between theory and experiment: intersections between theory and experiment
Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization
The lack of a priori distinctions between learning algorithms
Neural Computation
The existence of a priori distinctions between learning algorithms
Neural Computation
Bias/variance decompositions for likelihood-based estimators
Neural Computation
An Efficient Method To Estimate Bagging‘s Generalization Error
Machine Learning
Mining Dependence Structures from Statistical Learning Perspective
IDEAL '02 Proceedings of the Third International Conference on Intelligent Data Engineering and Automated Learning
On different facets of regularization theory
Neural Computation
Neural networks and the financial markets
Comparison of tree and graph encodings as function of problem complexity
Proceedings of the 9th annual conference on Genetic and evolutionary computation
On the Bayes fusion of visual features
Image and Vision Computing
Classifier ensembles: Select real-world applications
Information Fusion
Representing Case Variations for Learning General and Specific Adaptation Rules
Proceedings of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium
Invariant operators, small samples, and the bias-variance dilemma
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
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