Moreover, in Blum and Mitchell (1998), the co-training algorithm moves p highest confidence positive examples and n highest confidence negative examples from S to L, where p : n represents the class distribution in the original labeled training set (i.e., if there are 10 positive examples and 90 negative examples in the labeled set L, then p = 1 positive and n = 9 negative examples are moved to the labeled set at each iteration of co-training).
moreover, a great range of classifiers were used for this task, including: Naïve Bayes (Lewis and Ringuette, 1994), Knearest neighbors (Yang, 1999) and Support Vector machines (Joachims, 1998).