Are standard deviation estimates calculated via:
$ s_N = \sqrt{\frac{1}{N} \sum_{i=1}^N (x_i - \overline{x})^2}. $
(http://en.wikipedia.org/wiki/Standard_deviation#Sample_standard_deviation)
for prediction accuracies sampled from 10-fold cross validation? I'm concerned that the prediction accuracy calculated between each fold are dependent because of the substantial overlap between training sets (although the prediction sets are independent). Any resources that discuss this would be very helpful.
