Theory and applications of cross-data-type machine learning methods

我们研究了具有各种类型正则化的多核学习（MKL）的统计收敛特性。此外，我们提出了一个贝叶斯变体的MKL，并表明它具有最优性，而没有强的假设，在传统的理论分析MKL的设计假设。我们还提出了一个在线优化方法，这是有用的结构化正则化。结果表明，该算法收敛于最小-最大最优速率。

We have investigated statistical convergence properties of Multiple Kernel Learning (MKL) with various types of regularizations. Moreover, we proposed a Bayesian variant of MKL and showed that it has optimality without strong assumptions on the design which are assumed in conventional theoretical analysis of MKL. We also proposed an online optimization method that is useful for structured regularization. It was shown that the proposed algorithm converges in the mini-max optimal rate.