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Bias–variance tradeoff

Known as: Bias–variance dilemma, Bias–variance decomposition, Bias and variance tradeoff 
In statistics and machine learning, the bias–variance tradeoff (or dilemma) is the problem of simultaneously minimizing two sources of error that… Expand
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Highly Cited
2012
Highly Cited
2012
 
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Highly Cited
2009
Highly Cited
2009
Algorithms based on upper confidence bounds for balancing exploration and exploitation are gaining popularity since they are easy… Expand
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2007
2007
Given any generative classifier based on an inexact density model, we can define a discriminative counterpart that reduces its… Expand
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Highly Cited
2004
Highly Cited
2004
Bias-variance analysis provides a tool to study learning algorithms and can be used to properly design ensemble methods well… Expand
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Highly Cited
2002
Highly Cited
2002
Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. In this paper, the… Expand
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Highly Cited
2000
Highly Cited
2000
Temporal difference (td) algorithms are used in reinforcement learning to compute estimates of the value of a given policy in an… Expand
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Highly Cited
1999
Highly Cited
1999
The statistics of photographic images, when represented using multiscale (wavelet) bases, exhibit two striking types of non… Expand
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1998
1998
We propose a new in-sample cross validation based method (randomized GACV) for choosing smoothing or bandwidth parameters that… Expand
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Highly Cited
1997
Highly Cited
1997
One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated… Expand
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Highly Cited
1987
Highly Cited
1987
Strategies for hypothesis testing in scientific investigation and everyday reasoning have interested both psychologists and… Expand
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