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.

本文提出了一种学习方案，通过可扩展地结合多个基于单核的在线方法来减少内核选择偏差，从而扩展了单核解空间，增加了找到高性能解的可能性，并在累积正则化最小二乘成本指标方面实验证明所提出的学习方案优于单独使用的组合单核在线方法。