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With regard to the DE method, the level of SNP pruning that was suggested by So et al seems to be dependent on SNP density, and future research should explore whether the performance of the DE method can be further improved when the optimal level of pruning is considered to be a meta parameter whose value needs to be set through cross validation guided by the prediction error.
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During the training, we used the following meta parameters: (learning rate) γ=0.02, (regularization factor) λ=0.025 and ε=0.00001.
The other meta-parameter we had to tune was the pooling size r.
In this case the meta-parameter is varied and the cross-validated prediction error is determined for each setting.
Experiments show that only few Boosting meta-parameters increase accuracy, while meta-parameters derived from forecast combination research outperform others.
The same set of sentences was used for tuning the meta-parameters of the various network configurations.
The method incorporates hybrid support vector regression and genetic algorithm technique (SVR-GA) for efficient tuning of SVR meta-parameters.
Furthermore, this paper discusses the impact of meta-parameters in model selection and compares the performance of time-dependent GDM in different plume conditions.
A possibility to overcome this difficulty is considered on the example of a proposed meta-model generating autoencoders from a vector of meta-parameters of much smaller dimension than the number of autoencoder parameters.
Moreover, marginalized SDA only has two free meta-parameters, controlling the amount of noise as well as the number of layers to be stacked, which greatly simplifies the model selection process.
Table 1 Meta-parameters used in estimation of spectrogram features:,,,,, are number of pixels used to define TF windows of,, and, respectively; and show spectrogram frequency and time resolution; —sampling frequency.
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CEO of Professional Science Editing for Scientists @ prosciediting.com