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Different to independent and sequential learning, negative correlation learning trains all learners in an ensemble simultaneously and cooperatively with direct interactions. In negative correlation learning, each learner can be learned by the error signals only based on the differences between the output of the ensemble and the target output on a given example without considering whether itself has...
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...
Two error bounds were introduced in the learning process of balanced ensemble learning. They are the lower bound of error rate (LBER) and the upper bound of error output (UBEO) on the training set, respectively. These two error bounds would decide whether a training data point should be further learned or not after balanced ensemble learning has reached certain stage. Before the error rates are higher...
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...
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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