Adaptive Kernel Optimization for Probabilistic Learning: Integrating Support Vector Machines with Gaussian Process Frameworks
Tatiraju.V.Rajani Kanth, Balveer Singh, Yaspal Singh, Sunil Bhutada, Rohita Yamaganti · 2025
With its robust capabilities for non-linear regression and classification, kernel-based learning has emerged as a fundamental component of state-of-the-art machine learning approaches. In order to improve probabilistic learning, this study investigates Adaptive Kernel Optimization (AKO), a new method that combines the best features of the Support Vector Machine (SVM) and the Gaussian Process (GP) frameworks. Achieving better flexibility in modeling complicated data distributions while keeping computational efficiency is achieved by employing adaptive kernel functions in the suggested strategy. Quantifying uncertainty in addition to deterministic SVM classifications is made possible with the incorporation of GP kernels, which offer probabilistic insights. The suggested approach guarantees resilience across varied and high-dimensional datasets by dynamically adjusting kernel parameters according to data properties. Extensive testing on benchmark datasets shows that, in comparison to conventional SVM and GP approaches, our model generalizability, classification accuracy, and interpretability are much improved. Autonomous systems, healthcare diagnostics, and financial sectors can all benefit from the scalable, adaptive, and probabilistic learning models that this study establishes.