K-LR Modeling with Neural Economy and Its Utilization in Unclear Data

Abstract

In this chapter, we explain the K-LR model, a cognitive hierarchical model that combines linear regression and k-means clustering to improve the interpretability and generalization performance of linear models. We explain the principles of cognitive hierarchy models and provide examples of how K-LR can be applied to logistic regression and supply chain optimization. We also created a dynamic K-LR model with stochastic gradient descent, which allows the model to adapt to changes in the data over time and improve its accuracy and generalization performance. Finally, we propose some possible extensions and alternatives to K-LR, e.g. B. The integration of nonlinear functions and deep learning architectures is discussed, as well as the limitations and trade-offs of these approaches.