# Radial basis function kernel optimization for Support Vector Machine classifiers

arXiv: https://arxiv.org/abs/2007.08233 · HTML: https://mlsanity.com/paper/2007.08233/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 7 of 7)

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- [Extending the Abstraction of Personality Types based on MBTI with Machine Learning and Natural Language Processing](https://mlsanity.com/paper/2105.11798/) (2021)
- [Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines](https://mlsanity.com/paper/2310.10910/) (2023)
- [A study of the impact of generative AI-based data augmentation on software metadata classification](https://mlsanity.com/paper/2310.13714/) (2023)
- [Beyond the Gegenbauer Paradigm: q-Orthogonal Kernels for Machine Learning](https://mlsanity.com/paper/2608.03482/) (2026)

## Abstract (arXiv)

Support Vector Machines (SVMs) are still one of the most popular and precise classifiers. The Radial Basis Function (RBF) kernel has been used in SVMs to separate among classes with considerable success. However, there is an intrinsic dependence on the initial value of the kernel hyperparameter. In this work, we propose OKSVM, an algorithm that automatically learns the RBF kernel hyperparameter and adjusts the SVM weights simultaneously. The proposed optimization technique is based on a gradient descent method. We analyze the performance of our approach with respect to the classical SVM for classification on synthetic and real data. Experimental results show that OKSVM performs better irrespective of the initial values of the RBF hyperparameter.