An autoregressive 'sequence' model fed with per-label probability scores improves constrained multi-label classification on most tested datasets and learns logical constraints directly from the data.
In: ICML (2010)
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Constraint-aware Learning of Probabilistic Sequential Models for Multi-Label Classification
An autoregressive 'sequence' model fed with per-label probability scores improves constrained multi-label classification on most tested datasets and learns logical constraints directly from the data.