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A classifier is designed on the training set, and tested on, for.
The classifiers were designed on the training data set.
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This program will consist of combined aerobic 20-300 minutes) and resistance training, designed on the basis of our previous experience with pediatric patients with cancer, and following the rules of the institutions for global benchmarks for strength training and aerobic EX in children and adolescents [ 7, 14, 22].
Simulation of Clinical Cases has been designed for the training of professional workers on international standard protocols and procedures for chronic heart failure and peritoneal dialysis.
Guidance on the design of the training stage of a classification typically calls for the use of a large sample of randomly selected pure pixels in order to characterise the classes.
Theoretically, the optimal model is the simplest possible model designed on training data which shows the highest possible performance on any other equally representative set of testing data.
Man/machine interfaces are often designed on patient training data (e.g. electromyographic or electroencephalographic time series) from one session.
The design of the training was based on the training model used in the United Kingdom (UK), after a visit from the person in charge of the UK national training programme.
Add design on the train's body.
Each run of the 3-fold cross-validation algorithm consisted of a classifier designed on two data subsets (training) while testing was performed on the remaining subset; this is repeated three times.
We designed our predictor based on the training datasets (using 5-fold cross-validation protocol) and then we performed evaluation and comparison with the existing methods on the independent test datasets.
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