Considering that in the literature there is a very limited number of studies proposing new SVM kernels especially in regression problems, the scope of this research is to investigate the development of a novel Support Vector Machine Kernel. The proposed new W-SVM (Weighted-SVM) kernel was developed by applying a suitably transformed weight vector derived from particle swarm optimized neural networks in order to satisfy the kernel conditions of Mercer’s theorem and then incorporated to a Bayesian Optimized (BO) kernel for building the new proposed W-SVM kernel. The proposed SVM kernel was applied in Gross Domestic Product growth forecasting. The new kernel has led to significantly improved forecasting results compared to all the other conventional ANN, SVM, and optimized BO-SVM, PSO-ANN machine learning models.

考虑到文献中提出新的SVM内核（特别是在回归问题中）的研究非常有限，因此该研究的范围是研究新型支持向量机内核的开发。拟议的新W-SVM（Weighted-SVM）内核是通过应用从粒子群优化神经网络派生的适当变换的权重向量来开发的，以满足Mercer定理的内核条件，然后将其合并到贝叶斯优化（BO）内核中以用于构建新提出的W-SVM内核。所提出的SVM内核已应用于国内生产总值增长预测中。与所有其他传统的ANN，SVM和优化的BO-SVM，PSO-ANN机器学习模型相比，新内核已大大改善了预测结果。