The general concept of supervised learning and unsupervised learning is very clear.
In supervised learning, the decision on the unlabeled data is done after learning a classifier using available training samples, as examples of supervised classifiers we have decision tree, neural network, support vector machine(SVM).
Whereas, in an unsupervised system, the classifier does not have any labeled sample. In this later case, the classification is done by exploiting some criteria like Euclidean distance, a common example of the unsupervised classification method is the k-means cluster classifier.
But what are these learning algorithms in connectionist?