Structured learning is the subfield of machine learning concerned with computer programs that learn to map inputs to arbitrarily complex outputs. 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.[1]
Algorithms and models for structured learning include inductive logic programming, structured SVMs, conditional random fields, Markov logic networks and Constrained Conditional Models.