Ensemble averaging (machine learning)

Known as: Ensemble, Ensemble average (machine learning), Ensemble averaging 
In machine learning, particularly in the creation of artificial neural networks, ensemble averaging is the process of creating multiple models and… (More)
Wikipedia

Related topics

Related topics

7 relations

Broader (1)

Artificial intelligence
Committee machineEnsemble learningGlossary of artificial intelligenceMachine learning
(More)

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2014
Highly Cited
2014
The dropout learning algorithm
Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent… (More)
Is this relevant?
Highly Cited
2005
Highly Cited
2005
A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications
In this paper, we investigate several state-of-the-art machine-learning methods for automated classification of clustered… (More)
  • figure 1
  • figure 2
  • figure 3
  • table II
  • table III
Is this relevant?
2004
2004
Time-domain noise analysis of linear time-Invariant and linear time-variant systems using MATLAB and HSPICE
A custom simulation tool that combines HSPICE and MATLAB to enable time-domain noise analysis is reported. The simulation… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 5
  • figure 4
Is this relevant?
Highly Cited
2002
Highly Cited
2002
Calculation of Average, Uncertainty Range, and Reliability of Regional Climate Changes from AOGCM Simulations via the ‘‘Reliability Ensemble Averaging’’ (REA) Method
The ‘‘reliability ensemble averaging’’ (REA) method for calculating average, uncertainty range, and a measure of reliability of… (More)
  • table 1
  • figure 1
  • figure 2
  • figure 3
  • figure 4
Is this relevant?
Highly Cited
2000
Highly Cited
2000
Obtaining single stimulus evoked potentials with wavelet denoising
We present a method for the analysis of electroencephalograms (EEG). In particular, small signals due to stimulation, so-called… (More)
  • figure 1
  • figure 2
  • figure 3
Is this relevant?
2000
2000
Nonlinear adaptive filtering of stimulus artifact
Noninvasive measurements of somatosensory evoked potentials have both clinical and research applications. The electrical artifact… (More)
  • table 1
Is this relevant?
2000
2000
Overcoming selective ensemble averaging: unsupervised identification of event-related brain potentials
Presents a novel approach to the problem of event-related potential (ERP) identification, based on a competitive artificial… (More)
  • figure 1
  • figure 3
  • figure 4
  • figure 6
  • figure 7
Is this relevant?
Highly Cited
2000
Highly Cited
2000
Photon antibunching in single CdSe/ZnS quantum dot fluorescence
We investigate the ̄uorescence intensity correlation function of a single CdSe quantum dot (QD) using a start±stop experiment… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
Is this relevant?
Highly Cited
1998
Highly Cited
1998
Applying Neural Network Models to Prediction and Data Analysis in Meteorology and Oceanography.
Empirical or statistical methods have been introduced into meteorology and oceanography in four distinct stages: 1) linear… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
1997
Highly Cited
1997
Optimal ensemble averaging of neural networks
Based on an observation about the different effect of ensemble averaging on the bias and variance portions of the prediction… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?