W10-3204 |
we can obtain three groups of
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two-fold cross-validation
|
data sets . Estimating the parameter
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S13-1033 |
whole training set by means of
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two-fold cross-validation
|
. The individual and global results
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P13-1039 |
models , tuning hyperparameters by
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two-fold cross-validation
|
. We then extracted noun phrase
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W10-3204 |
experiments , three groups of
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two-fold cross-validation
|
sets are used to estimate the
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N13-1112 |
-- 5 ) for testing . We perform
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two-fold cross-validation
|
experiments using the two test
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W10-3204 |
) model to train and test on a
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two-fold cross-validation
|
data set . The extracted features
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W05-0609 |
the three sets and ten runs of
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two-fold cross-validation
|
for each of them . For SDC ,
|
W13-2220 |
regularization parameter C is chosen by
|
two-fold cross-validation
|
. In practice , subsampling of
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J10-2002 |
reported here is the average of
|
two-fold cross-validation
|
. We compared three methods :
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N13-1112 |
and Discussion Table 3 shows the
|
two-fold cross-validation
|
results for our 14-class temporal
|
D15-1054 |
0.2 through grid search based on
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two-fold cross-validation
|
. This small value indeed verifies
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W05-0405 |
articles in half , and perform
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two-fold cross-validation
|
as recommended by Dietterich
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J11-1007 |
explained in Section 5 , with
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two-fold cross-validation
|
when parsing the training data
|
N12-1003 |
halves and conducted tests with
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two-fold cross-validation
|
. We tested thresholds for the
|
W10-1746 |
2002 ) with a RBF ker - nel .
|
Two-fold cross-validation
|
was done to prevent over-fitting
|