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Robust Recurrent Kernel Online Learning
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DOI：10.1109/TNNLS.2016.2518223.png)
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We propose a robust recurrent kernel online learning (RRKOL) algorithm based on the celebrated real-time recurrent learning approach that exploits the kernel trick in a recurrent online training manner. The novel RRKOL algorithm guarantees weight convergence with regularized risk management through the use of adaptive recurrent hyperparameters for superior generalization performance. Based on a new concept of the structure update error with a variable parameter length, we are the first one to propose the detailed structure update error, such that the weight convergence and robust stability proof can be integrated with a kernel sparsification scheme based on a solid theoretical ground. The RRKOL algorithm automatically weighs the regularized term in the recurrent loss function, such that we not only minimize the estimation error but also improve the generalization performance through sparsification with simulation support.
Keyword:
Kernel online learning
real-time recurrent learning (RTRL)
sparsification
weight convergence and robust stability
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