Tensor-Based Foundations of Ordinary Least Squares and Neural Network Regression Models
This provides a new theoretical foundation for regression models, but it appears incremental as it refines existing methods rather than introducing a new paradigm.
The paper tackles the mathematical development of Ordinary Least Squares and Neural Network regression models by using Tensor Analysis and matrix computations, resulting in three algorithms including a streamlined Backpropagation Algorithm.
This article introduces a novel approach to the mathematical development of Ordinary Least Squares and Neural Network regression models, diverging from traditional methods in current Machine Learning literature. By leveraging Tensor Analysis and fundamental matrix computations, the theoretical foundations of both models are meticulously detailed and extended to their complete algorithmic forms. The study culminates in the presentation of three algorithms, including a streamlined version of the Backpropagation Algorithm for Neural Networks, illustrating the benefits of this new mathematical approach.