Polynomial kernel learning for interpolation kernel machines with application to graph classification

Since all training data is interpolated, interpolating classifiers have zero training error. However, recent work provides compelling reasons to investigate these classifiers, including their significance for ensemble methods. Interpolation kernel machines, which belong to the class of interpolating classifiers, are capable of good generalization and have proven to be an effective substitute for support vector machines, particularly for graph classification. In this work, we further enhance their performance by studying multiple kernel learning. To this end, we propose a general scheme of polynomial combined kernel functions, employing both quadratic and cubic kernel combinations in our experimental work. Our findings demonstrate that this approach improves performance compared to individual graph kernels. Our work supports the use of interpolation kernel machines as an alternative to support vector machines, thereby contributing to greater methodological diversity. • Enhanced performance of interpolation kernel machines (a good alternative to SVMs) • General framework of polynomial combined kernel functions for performance enhancement • Experimental results demonstrate the performance improvement potential of our approach