Improving Support Vector Machine using Modified Kernel Function

In machine learning, support vector machine (SVM) classifiers are highly effective in classification and pattern recognition

tasks. The kernel function plays a crucial role in SVM, as it maps data into higher dimensional space to enable linear

separability. This research proposes a modified kernel function, the Combined Polynomial Radial Basis Function (PRBF),

that combines the strengths of Gaussian Radial Basis Function (RBF) and Polynomial kernels to improve the performance of

SVM. The PRBF model aims to enhance classification accuracy by capturing patterns in the data at both local and global

levels. The results demonstrate that the PRBF outperforms traditional RBF and Polynomial kernels across various datasets,

achieving higher accuracy, sensitivity, and specificity. the PRBF kernel represents a significant advancement in SVM classifier

technology, merging the advantages of RBF and Polynomial kernels to deliver enhanced accuracy, sensitivity, and specificity.

This study validates that PRBF as a superior alternative for enhancing SVM performance in diverse applications in different

domain.