Structured Prediction

Structured prediction is a branch of machine learning that focuses on representations of spaces with combinatorial structures and algorithms for reasoning and parameter estimation over such spaces. Core methods in this field include exactly solvable techniques like dynamic programming and spanning tree algorithms, as well as heuristic techniques such as linear programming relaxations and greedy search. Structured prediction aims to improve the accuracy and robustness of predictive models by capturing dependencies among complex data, offering broad application value.