A Parallel Way to Select the Parameters of SVM Based on the Ant Optimization Algorithm
This is an incremental improvement for researchers in machine learning, focusing on parameter tuning in SVM applications like text classification and image recognition.
The paper tackles the problem of optimizing Support Vector Machine (SVM) parameters by combining Ant Colony Optimization (ACO) with a parallel algorithm to find better parameters, but no concrete results or numbers are provided.
A large number of experimental data shows that Support Vector Machine (SVM) algorithm has obvious advantages in text classification, handwriting recognition, image classification, bioinformatics, and some other fields. To some degree, the optimization of SVM depends on its kernel function and Slack variable, the determinant of which is its parameters $δ$ and c in the classification function. That is to say,to optimize the SVM algorithm, the optimization of the two parameters play a huge role. Ant Colony Optimization (ACO) is optimization algorithm which simulate ants to find the optimal path.In the available literature, we mix the ACO algorithm and Parallel algorithm together to find a well parameters.