论文标题
深度对数据科学的自我优化学习
Depth Self-Optimized Learning Toward Data Science
论文作者
论文摘要
我们提出了一个两阶段的模型，称为Depth自我优化学习（DSOL），旨在实现ANN深度自我配置，自我优化以及无手动干预的ANN培训。在DSOL的第一阶段，它将根据特定数据集配置特定深度的ANN。在第二阶段，DSOL将根据强化学习（RL）不断优化ANN。最后，将最佳深度返回到DSOL的第一阶段进行培训，以便DSOL可以在再次处理相似数据集时配置适当的ANN深度并执行更合理的优化。在实验中，我们在虹膜和波士顿外壳数据集上运行了DSOL，结果表明DSOL的性能很好。我们已将实验记录和代码上传到我们的GitHub。
We propose a two-stage model called Depth Self-Optimized Learning (DSOL), which aims to realize ANN depth self-configuration, self-optimization as well as ANN training without manual intervention. In the first stage of DSOL, it will configure ANN of specific depth according to a specific dataset. In the second stage, DSOL will continuously optimize ANN based on Reinforcement Learning (RL). Finally, the optimal depth is returned to the first stage of DSOL for training, so that DSOL can configure the appropriate ANN depth and perform more reasonable optimization when processing similar datasets again. In the experiment, we ran DSOL on the Iris and Boston housing datasets, and the results showed that DSOL performed well. We have uploaded the experiment records and code to our Github.
