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The GA was used to optimize pattern recognition performance on training sets using a figure of merit designed to maximize correct classification of acceptable samples and minimize classification of unacceptable samples or samples of dissimilar materials.
NCA (Goldberger et al., 2005) learns a linear transformation of the feature space to optimize leave-one-out (LOO) performance on training data for k-nearest neighbor (k-NN) classifiers.
(a) Accuracy performance on training data.
(c) F-score performance on training data.
Observers' performance on training trials was extremely consistent throughout the training.
Another challenge of using microarray data for prediction is overfitting, where classification models may not correctly predict new data despite good performance on training datasets [ 23, 24].
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This process is essential to prevent unlucky distributions of training and testing datasets and to prevent the overall classifier from overfitting its performance on one training set.
The F1 measure gave us a quantitative idea of the performance of each method on training data.
We also note here that while PCA methods have similar performances between performances on training and test data, MDA and MGD require regularization to prevent over-fitting of training data sets.
ANOVAs with Novelty and Day as within-factors were performed on training performance, competition wins, and post-competition CORT concentration.
A high-performance on-line training radial basis function network-sliding mode (RBFNSM) algorithm is designed to derive the turbine speed to extract maximum power from the wind.
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