# Isn't leave-1-out insufficient for proper classification evaluation?

I encountered several papers that used some classification method (for instance, LDA), with leave-1-out validation, and posted the classification results as an aggregation of all results (for all samples 'left out').

I was wondering if this weren't a little problematic. I'm used to putting, say, 20% of samples as test and then training the classifier on the remaining 80%. But in the leave-1-out, the case is more similar to 99% training and 1% testing (in the case of 100 samples, for the sake of example)... so how can I properly compare my method to leave-1-out?

Thanks.