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In conclusion, there exists a relation between the defined AD and the impact of training samples in characterising it based on their threshold values.
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Another factor to take into account is the impact of the number of training samples on the performance of HDLA and ORSM.
Sensitivity analysis of parameters of training samples to select the largest impact was performed with C5.0 decision tree to obtain a WT-trained set.
Dependence on the number of training samples.
Also to evaluate the ability of EANN trained by smaller training data set, three data division strategies with different number of training samples were considered for the training purpose.
The number of training samples has complex influence on the prediction performance of the trained network.
The number of training samples is, in practice, always limited.
where M is the number of training samples.
Feature vectors and class labels of training samples are stored in the training phase.
R is the autocorrelation matrix of training samples.
The number of training samples varied in each simulation.
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