Kernel online learning algorithm with state feedbacks

This paper presents a novel recurrent kernel algorithm for online learning. It introduces a propagation scheme to recycle the kernel state information. The novel structure keeps records of the training sample information and incorporates it in the learning task over time to preserve the characteristics of the training sequences. In order to ensure the convergence of the algorithm, an adaptive training method is proposed to tune the kernel weight and recurrent weight simultaneously followed by detailed analysis of the weight convergence. Numerical simulations are presented to show the effectiveness of the proposed algorithm.