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Different to other re-sampling ensemble learning, negative correlation learning trains all individual models in an ensemble simultaneously and cooperatively. In negative correlation learning, each individual could see all training data, and adapt its target function based on what the rest of individuals in the ensemble have learned. In this paper, two error bounds are introduced in negative correlation...
Balanced ensemble learning is developed from negative correlation learning by shifting the learning targets. Compared to the negative correlation learning, balanced ensemble learning is able to learn faster and achieve the higher accuracy on the training sets for a number of the tested classification problems. However, it has been found that the higher accuracy balanced ensemble learning obtained...
Ensemble learning system could lessen the degree of overfitting that often appear in the supervised learning process for a single learning model. However, overfitting had still been observed in negative correlation learning that is an ensemble learning method with correlation-based penalty. Two constraints were introduced into negative correlation learning in order to conquer such overfitting. One...
Researchers tend to agree that an increasing quantity of data has caused the complexity and difficulty for information discovery, management and reuse. An essential factor relates to the increasing channels for information sharing. Finding information, especially those meaningful or useful one, that meets ultimate task of user becomes harder then it is used to be. In this research, issues concerning...
It has been proved that there is a bias-variance-covariance trade-off among the trained neural network ensembles. In this paper, extra learning on random data points was proposed to control the variations of the correlations in the negative correlation learning (NCL). Without the control of the correlations, NCL might have arbitrary values on the unknown data points after learning too much on the...
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