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However, the former classifiers have not been trained by the error subset of later classifiers.
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Only using the classification error subset of former classifiers to training later classifiers is not enough.
However, as Winkler has pointed out, comparison weights for individual data items are strongly influenced by the error rates for those data items in each data collection, or in particular subsets of data collections [ 50].
Keller was mortified by the error.
And, of course, some of the very best music was realized by the latter subset.
The training was terminated if the error in the validation subsets stopped dropping or, indeed, started to rise (early stopping).
We report the best subset under each correlation pattern (SO), its error rate, and the error rate for the best subset chosen ignoring correlation (SI).
Once the network with the smallest validation error is chosen, the error is computed for the test subset in order to assess its performance on previously unseen data.
This distribution is most likely influenced by the classification error discussed previously; however, the genomewide distribution may serve as a baseline level for comparison with subsets of genes.
We also estimated the probability that the subset errors arose by chance (a 'label permutation' as discussed in Materials and methods).
Given an initial large set of neural networks, our approach is aimed to select the subset formed by the most error-independent nets.
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