Advanced · Data
Constrained conditional model (CCM)
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A constrained conditional model (CCM) is a machine learning framework that combines probabilistic or discriminative models with declarative constraints.
Technical Definition
A constrained conditional model (CCM) is a machine learning framework that combines probabilistic or discriminative models with declarative constraints.
How it works
A constrained conditional model (CCM) augments traditional machine learning models, which learn conditional probabilities or discriminative functions, with explicit constraints. These constraints, often expressed declaratively, help guide the learning and inference process to produce solutions that adhere to specific rules or properties. This is particularly useful in domains where solutions must satisfy certain logical or physical requirements.
Related Concepts
- Inference — Using a trained model to make predictions on new data — the deployment phase of machine learning.
- Machine Learning — A field of AI where systems learn patterns from data instead of following hard-coded rules.
- Constraint programming — Constraint programming is a paradigm where problems are solved by stating relationships among variables in the form of constraints.