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… (More)
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Topic mentions per year

Topic mentions per year

2005-2016
0120052016

Papers overview

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2016
2016
Careful model evaluation is essential for using the results of data-driven land cover change (LCC) models. A useful evaluation… (More)
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2013
2013
It has been recognized that, when an information retrieval (IR) system achieves improvement in mean retrieval effectiveness (e.g… (More)
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2011
2011
Bias variance decomposition of the expected error defined for regression and classification problems is an important tool to… (More)
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2009
2009
Bias-variance decomposition is known to be a powerful tool when explaining the success of learning methods. By now it’s common… (More)
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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… (More)
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2006
2006
A new combination model, which incorporated observational learning with Behavior Knowledge Space (BKS) method, was proposed in… (More)
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2005
2005
One of the most important issues for computational methods is their time complexity. This paper introduces a temporal MDL… (More)
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