Structured learning is the subfield of machine learning concerned with computer programs that learn to map inputs to outputs, both from arbitrarily complex sets. This stands in contrast to the simpler approaches of classification, where input data (instances) are mapped to "atomic" labels, i.e. symbols without any internal structure, and regression, where inputs are mapped to scalar numbers or vectors.