As far as I understand your question, it can be formulated this way:
Instead of calculating a quality measure for each of the k validation-folds and then calculate the average, may I aggregate all folds an then calculate my quality measure, hence getting only one instead of k values?
This question requires two perspectives:
From the perspective of crossvalidation itself it is ok, because training- and testsamples still have an empty intersection etc. Since you just aggregate multiple samples drawn without replacement, the test-distribution is not spoiled.
From the perspective of the model, it depends whether the model produces comparable scores. SVM will work in my opinion, but imagine a model which min-max-normalizes the scores across the test-set (iiek), so that the calculation of a representative decision threshold across all test-samples will be quite hard.
In general, a lot of techniques which require the estimation of a parameter, which itself depends on the quality of a model, utilize this approach. A concrete example is the calculating of an operator to calibrate the scores of a classification model, e.g. Platt Scaling.
Furthermore (also this not an completely satisfying argument) the open source software Rapidminer has an operator for this approach.
I want to point out, that although this approach is useful to get reliable quality measures for only small datasets, it may be hard to perform statistical tests to compare the significance of two models, since cv cannot be repeated endlessly (example: how to estimate whether the assumption of the so-often-misused t-test is satisfied if you only have 6 data points?).
PPS: Also interested, I was not able to find a paper focusing on the examination of this approach. The papers I have seen so far using this technique did not bother to reference it.