  Multi-Output Dependence (MOD) learning is a generalization of standard
classification problems that allows for multiple outputs that are dependent on
each other. A primary issue that arises in the context of MOD learning is that
for any given input pattern there can be multiple correct output patterns. This
changes the learning task from function approximation to relation
approximation. Previous algorithms do not consider this problem, and thus
cannot be readily applied to MOD problems. To perform MOD learning, we
introduce the Hierarchical Multi-Output Nearest Neighbor model (HMONN) that
employs a basic learning model for each output and a modified nearest neighbor
approach to refine the initial results.
