Patent attributes
Machine learning based method for multilabel learning with label relationships is provided. This methodology addresses the technical problem of alleviating computational complexity of training a machine learning model that generates multilabel output with constraints, especially in contexts characterized by a large volume of data, by providing a new formulation that encodes probabilistic relationships among the labels as a regularization parameter in the training objective of the underlying model. For example, the training process of the model may be configured to have two objectives. Namely, in addition to the objective of minimizing conventional multilabel loss, there is another training objective, which is to minimize penalty associated with the prediction generated by the model breaking probabilistic relationships among the labels.
