The performance of reproducing kernel Hilbert space-based methods is known to be sensitive to the choice of the reproducing kernel. Choosing an adequate reproducing kernel can be challenging and computationally demanding, especially in data-rich tasks without prior information about the solution domain. In this paper, we propose a learning scheme that scalably combines several single kernel-based online methods to reduce the kernel-selection bias. The proposed learning scheme applies to any task formulated as a regularized empirical risk minimization convex problem. More specifically, our learning scheme is based on a multi-kernel learning formulation that can be applied to widen any single-kernel solution space, thus increasing the possibility of finding higher-performance solutions. In addition, it is parallelizable, allowing for the distribution of the computational load across different computing units. We show experimentally that the proposed learning scheme outperforms the combined single-kernel online methods separately in terms of the cumulative regularized least squares cost metric.

翻译：基于再生核希尔伯特空间的方法的性能已知对再生核的选择高度敏感。选择适当的再生核可能具有挑战性且计算成本高昂，尤其是在缺乏解域先验信息的数据密集型任务中。本文提出一种学习方案，该方案可扩展地结合多个单核在线方法，以减少核选择偏差。所提出的学习方案适用于任何可表述为正则化经验风险最小化凸问题的任务。具体而言，我们的学习方案基于一种多核学习框架，该框架可扩展任何单核解空间，从而提高发现更高性能解的可能性。此外，该方案可并行化，允许将计算负载分布到不同计算单元上。实验表明，所提出的学习方案在累积正则化最小二乘代价指标方面，分别优于组合的单个单核在线方法。