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As the basis of image recognition, such information can provide sample training and quick identification.
After collecting the fluorescent images of the soybean milk samples, the top seven wavelet moment invariants are selected according to the sample training and experimental comparison.
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Both contexts involved match-to-sample training and testing with abstract stimuli, but one involved referring to the stimuli as the names and sounds of "animals".
Intuitively, the proposed sampling, training, and prediction framework can be applied on any existing neuron tracing algorithms to test and improve its performance.
To estimate potential improvements in predictive performance of the nominally best predictors for each classification problem, we pooled all cases and carried out a series of split-sample training and validation analyses in which the predictors were trained on increasingly larger data sets.
Furthermore, the random sampling, training and test procedures were repeated 50 times to get the mean performance measures of the logistic regression model.
As discussed in the section on overfitting, this motivates splitting the data into two random samples, training and test, and using the regions from the training sample to compute the definitive ROC curve based on data in the test sample.
On average, across 100 randomly sampled training and validation sets, model 7 had a significantly (p <0.0001) greater prediction correlation coefficient (r =0.86 ± 0.01) than model 1 (r =0.83 ± 0.01; Additional file 2: Figure S1).
Two municipal solid waste incinerators (MWIs) were selected for conducting flue gas sampling to determine the particulate distribution and heavy metals concentration on particles with different sizes by US EPA Method 5 sampling train and a cascade impactor.
Four edge orientations were used in the sample training task: 0∘, 45∘, 90∘ and 135∘.
For each of the settings, we simulated 100 samples for training and 100 samples for test.
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