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Fixed some typos in the spec files.
--HG-- extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402963
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@ -325,7 +325,7 @@ namespace dlib
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given binary classification problem for the given number of folds.
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Each fold is tested using the output of the trainer and the average
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classification accuracy from all folds is returned.
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- The accuracy is returned in a column vector, let us call it R. Both
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- The accuracy is returned in a row vector, let us call it R. Both
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quantities in R are numbers between 0 and 1 which represent the fraction
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of examples correctly classified. R(0) is the fraction of +1 examples
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correctly classified and R(1) is the fraction of -1 examples correctly
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@ -355,7 +355,7 @@ namespace dlib
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- dec_funct_type == some kind of decision function object (e.g. decision_function)
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ensures
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- tests the given decision function by calling on the x_test and y_test samples.
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- The test accuracy is returned in a column vector, let us call it R. Both
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- The test accuracy is returned in a row vector, let us call it R. Both
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quantities in R are numbers between 0 and 1 which represent the fraction
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of examples correctly classified. R(0) is the fraction of +1 examples
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correctly classified and R(1) is the fraction of -1 examples correctly
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@ -37,7 +37,7 @@ namespace dlib
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Each fold is tested using the output of the trainer and the average
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classification accuracy from all folds is returned.
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- uses num_threads threads of execution in doing the cross validation.
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- The accuracy is returned in a column vector, let us call it R. Both
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- The accuracy is returned in a row vector, let us call it R. Both
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quantities in R are numbers between 0 and 1 which represent the fraction
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of examples correctly classified. R(0) is the fraction of +1 examples
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correctly classified and R(1) is the fraction of -1 examples correctly
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