An investigation into the effects of multiple kernel combinations on solutions spaces in support vector machines

The use of Multiple Kernel Learning (MKL) for Support Vector Machines (SVM) in Machine Learning tasks is a growing field of study. MKL kernels expand on traditional base kernels that are used to improve performance on non-linearly separable datasets. Multiple kernels use combinations of those base kernels to develop novel kernel shapes that allow for more diversity in the generated solution spaces. Customising these kernels to the dataset is still mostly a process of trial and error. Guidelines around what combinations to implement are lacking and usually they requires domain specific knowledge and understanding of the data. Through a brute force approach, this study tests multiple datasets against a combination of base and non-weighted MKL kernels across a range of tuning hyperparameters. The goal was to determine the effect different kernels shapes have on classification accuracy and whether the resulting values are statistically different populations. A selection of 8 different datasets are chosen and trained against a binary classifier. The research will demonstrate the power for MKL to produce new and effective kernels showing the power and usefulness of this approach.