  Machine learning algorithms have made remarkable achievements in the field of
artificial intelligence. However, most machine learning algorithms are
sensitive to the hyper-parameters. Manually optimizing the hyper-parameters is
a common method of hyper-parameter tuning. However, it is costly and
empirically dependent. Automatic hyper-parameter optimization (autoHPO) is
favored due to its effectiveness. However, current autoHPO methods are usually
only effective for a certain type of problems, and the time cost is high. In
this paper, we propose an efficient automatic parameter optimization approach,
which is based on the mapping from data to the corresponding hyper-parameters.
To describe such mapping, we propose a sophisticated network structure. To
obtain such mapping, we develop effective network constrution algorithms. We
also design strategy to optimize the result futher during the application of
the mapping. Extensive experimental results demonstrate that the proposed
approaches outperform the state-of-the-art apporaches significantly.
