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This paper discusses how to apply the ensemble learning for the individual learners on the randomly splitting data. Rather than letting the individual learners learn independently on the different subsets, it would be better for the individual learners to learn cooperatively by exchanging the learned values. In this way, the individual learners could learn the whole given data together while they...
This paper proposes a hybrid negative correlation learning in which each individual neural network in an neural network ensemble would either learn a data point by negative correlation learning or learn to be different to the neural network ensemble. The implementation is through randomly splitting the training set into two subsets for each individual neural network in learning. On one subset of the...
In view of the defects in model of thermocouple characteristic using BP neural network (BPNN), such as lower precision, varying output, instability (after repeated training, the output may be queer), a model of thermocouple characteristic based on Generalized Regression Neural Network (GRNN) is established. The paper gives the process of model building for Ni-Cr Constantan thermocouple characteristic...
It is often that the learned neural networks end with different decision boundaries under the variations of training data, learning algorithms, architectures, and initial random weights. Such variations are helpful in designing neural network ensembles, but are harmful for making unstable performances, i.e., large variances among different learnings. This paper discusses how to reduce such variances...
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