A Constrained Conditional Model (CCM) is a framework in machine learning that combines the learning of conditional models with declarative constraints within a constrained optimization framework. These constraints can be either hard, which prohibit certain assignments, or soft, which penalize unlikely assignments. The constraints are used to incorporate domain-specific knowledge into the model, allowing for more expressive decision-making in complex output spaces.

• Medical diagnosis
• Financial forecasting
• Image segmentation
• Natural language processing
• Recommendation systems

For instance, in medical diagnosis, a CCM can be used to combine the learning of disease models with constraints on the probability of certain diagnoses given patient symptoms. The hard constraints could prohibit the model from assigning a patient a specific diagnosis if they do not have the necessary symptoms, while the soft constraints could penalize the model for assigning a diagnosis that is unlikely given the patient’s symptoms.

• Designing the semantic data model