General Regression Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, and Feedforward Neural Networks
This work addresses a specific parameter tuning problem for researchers and practitioners using these algorithms, but it is incremental as it focuses on comparing existing methods rather than introducing new ones.
The project aimed to determine optimal sigma values for maximizing F1 score and accuracy in machine learning models, specifically investigating whether these values are identical across General Regression Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, and Feedforward Neural Networks.
The aim of this project is to develop a code to discover the optimal sigma value that maximum the F1 score and the optimal sigma value that maximizes the accuracy and to find out if they are the same. Four algorithms which can be used to solve this problem are: Genetic Regression Neural Networks (GRNNs), Radial Based Function (RBF) Neural Networks (RBFNNs), Support Vector Machines (SVMs) and Feedforward Neural Network (FFNNs).