Nonparametric Online Machine Learning with Kernels

This study researched kernel-based methods and max-margin learning for largescale datasets. It advanced several theoretical and practical aspects of kernel-based and max-margin methods at the intersection with Bayesian modelling. New learning methods were proposed to avoid the curse of kernelisation while simultaneously yielding superior accuracy compared with state-of-the-art baselines.