The human user must possess knowledge/expertise in the problem domain, including the ability to consult/research authoritative sources when necessary.
There are situations in which unlabeled data is abundant but manual labeling is expensive.
During each iteration, i, T is broken up into three subsets Most of the current research in active learning involves the best method to choose the data points for TC,i.
Algorithms for determining which data points should be labeled can be organized into a number of different categories, based upon their purpose:[1] A wide variety of algorithms have been studied that fall into these categories.
Minimum Marginal Hyperplane methods assume that the data with the smallest W are those that the SVM is most uncertain about and therefore should be placed in TC,i to be labeled.