Skip to search formSkip to main content
You are currently offline. Some features of the site may not work correctly.

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
Wikipedia

Related topics

Related topics

24 relations
AlgorithmArtificial neural networkBoosting (machine learning)Bootstrap aggregating
Expand

Broader (3)

Machine learningModel selectionStatistical classification

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Review
2018
Review
2018
Understanding Hypothetical Bias: An Enhanced Meta‐Analysis
&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
Rainbow: Combining Improvements in Deep Reinforcement Learning
The deep reinforcement learning community has made several independent improvements to the DQN algorithm. However, it is unclear… Expand
  • figure 1
  • table 1
  • figure 2
  • table 2
  • figure 3
Highly Cited
2018
Highly Cited
2018
A Modern Take on the Bias-Variance Tradeoff in Neural Networks
The bias-variance tradeoff tells us that as model complexity increases, bias falls and variances increases, leading to a U-shaped… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Highly Cited
2012
Highly Cited
2012
Understanding the bias-variance tradeoff
 
Highly Cited
2006
Highly Cited
2006
SVM-KNN: Discriminative Nearest Neighbor Classification for Visual Category Recognition
We consider visual category recognition in the framework of measuring similarities, or equivalently perceptual distances, to… Expand
  • figure 1
  • table 1
  • table 2
  • figure 2
  • table 3
Highly Cited
2002
Highly Cited
2002
Ensembling neural networks: Many could be better than all
Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. In this paper, the… Expand
  • figure 1
  • table 1
  • table 2
  • figure 2
  • figure 3
Highly Cited
2000
Highly Cited
2000
Bias-Variance Error Bounds for Temporal Difference Updates
Temporal difference (td) algorithms are used in reinforcement learning to compute estimates of the value of a given policy in an… Expand
  • figure 1
  • figure 2
  • figure 3
Highly Cited
1997
Highly Cited
1997
Boosting the margin: A new explanation for the effectiveness of voting methods
One of the surprising recurring phenomena observed in experiments with boosting is that the test error of the generated… Expand
  • figure 1
  • figure 2
  • table 1
  • figure 6
  • table 2
Highly Cited
1994
Highly Cited
1994
Exploring the "planning fallacy": Why people underestimate their task completion times.
This study tested 3 main hypotheses concerning people's predictions of task completion times: (a) People underestimate their own… Expand
  • table 1
  • table 2
  • table 3
  • table 4
  • table 5
Highly Cited
1987
Highly Cited
1987
Confirmation, Disconfirmation, and Informa-tion in Hypothesis Testing
Strategies for hypothesis testing in scientific investigation and everyday reasoning have interested both psychologists and… Expand
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy, Terms of Service, and Dataset License
ACCEPT & CONTINUE