MLCPn
MLCPn, or Multi-Level Constraint Polynomial Network, is a theoretical framework in machine learning that extends polynomial modeling with a hierarchical, multi-level architecture and explicit structural constraints. It seeks to combine the expressive power of polynomial representations with the modular organization of deep networks while enforcing properties such as monotonicity, sparsity, and stability in parts of the model.
The architecture consists of several levels, each implementing a constrained polynomial map. At a given level,
Training MLCPn typically uses a hybrid optimization approach. A differentiable loss guides parameter updates, while regularization
Applications for MLCPn include system identification, control, time-series forecasting, and scientific modeling where data may be