# Cross validation in semi-supervised learning

With semi-supervised learning a labeled set $X_L$ and unlabeled set $X_U$ are given. If the learning algorithm has several free-parameters we are forced to perform cross-validation to try to guess them. Cross-validation can only be applied to the labeled set. So:

1. If we have very few labeled examples, say about 1%-10%, is it better to apply LOO-CV?

2. If the ratio of labeled samples is bigger (e.g. 50-60%), k-fold CV might be better (as LOO-CV can introduce a huge bias), but then, can we assume that the sets will be $i.i.d.$? Here is it better to pick a low k?

What is the best way validate a model in semi-supervised learning?

• I understand that recall is calculated using the labeled validation set and probability to predict class using labeled and unlabeled examples. How can I estimate the probability for a classifier to predict a class? Using simple probabilities such as $p_i = class_i/total$ would be okey? – KoTy Dec 5 '14 at 23:02