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Another implication is that the distance over which these folding forces act may be different for each fold.
For each fold, we fit the model to the remaining 4/5 of the data, and predicted the behavior in the held-out 1/5.
The method can (i) reduce the number of states using secondary structure information about proteins for each fold and (ii) recognize protein folds more accurately than other HMMs.
For each fold, we fit 100 trees.
In the bytes model case, both values are the same for each fold.
This is because, for each fold, the entire dataset apart from one image is used as the training set.
For each fold, 1/5th of the dataset served as test set, with 4/5th as training set.
The optimal values of K and J are highly dependent on the training examples, thus vary for each fold.
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For the initialization of each fold we give two pre-labeled instances from two distinct classes to the classifier.
Training and testing was performed with nested 10-fold cross validation and 6 10 features for each test fold.
Smooth the towel between each fold for the best result.
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