Feature Selection with L1 Regularization in Formal Neurons

Designing classifiers on high-dimensional learning data sets is an important task that appears in artificial intelligence applications. Designing classifiers for high-dimensional data involves learning hierarchical neural networks combined with feature selection. Feature selection aims to omit features that are unnecessary for a given problem. Feature selection in formal meurons can be achieved by minimizing convex and picewise linear (CPL) criterion functions with L1 regularization. Minimizing CPL criterion functions can be associated with computations on a finite number of vertices in the parameter space.