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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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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2018
Review
2018
&NA; The presence of hypothetical bias (HB) associated with stated preference methods has garnered frequent attention in the… Expand
Highly Cited
2018
Highly Cited
2018
The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear… Expand
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Highly Cited
2018
Highly Cited
2018
The bias-variance tradeoff tells us that as model complexity increases, bias falls and variances increases, leading to a U-shaped… Expand
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Highly Cited
2012
Highly Cited
2012
 
Highly Cited
2006
Highly Cited
2006
We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to… 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
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
1994
Highly Cited
1994
This study tested 3 main hypotheses concerning people's predictions of task completion times: (a) People underestimate their own… 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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