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This paper proposes a novel feature extraction method based on ensemble learning.
A MapReduce approach based on ensemble learning and data resampling can be found in [47].
We also proposed four models based on ensemble learning to improve the accuracy of each learning algorithm.
This study proposes a VFM system based on ensemble learning for fields with common metering infrastructure where data generated is very limited.
In this paper, we use infectious diseases as the domain of study to demonstrate a hierarchical learning method based on ensemble learning that attempts to address these issues through feature abstraction.
In this study, we proposed a novel variable selection framework based on ensemble learning, in which random forests (RF) [ 26] was chosen as the independent variable selector for creating the decision ensemble, where the number of trees was 500.
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We therefore employed a more sophisticated machine learning method originally developed for DNA microarray data analysis, based on the ensemble learning algorithm LogitBoost-NR as described in [ 10], to provide a second ranking of the genes.
As we focus on ensemble-based semi-supervised learning from imbalanced class distributions, specifically ensembles of self-training and co-training classifiers, we will first provide background on self-training and co-training, and also on ensemble learning.
This paper proposes a technique for classifying partial discharge (PD) patterns based on ensemble neural network (ENN) learning.
The forecast component is based on ensemble flow forecasting.
Calculation of accessibility is based on ensemble free energies.
More suggestions(12)
based on probabilistic learning
based on incremental learning
based on ensemble reconstruction
based on ensemble k
based on ensemble averaging
based on sociocultural learning
based on individual learning
based on ensemble flow
based on different learning
based on supervised learning
based on deep learning
based on statistical learning
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