论文标题
在调整深度学习模型上：数据挖掘观点
On tuning deep learning models: a data mining perspective
论文作者
论文摘要
深度学习算法因其节点的基本连接机制而异。它们具有各种通过特定算法或随机选择的超参数。同时，深度学习算法的超参数有可能帮助提高机器学习任务的性能。在本文中，为应对源自深度学习模型超参数的研究人员提供了调整指南。为此，根据调整和数据挖掘的角度研究了四种类型的深度学习算法。此外，在四种深度学习算法上评估了超参数的常见搜索方法。根据这项研究的结果，归一化有助于提高分类的性能。功能的数量并没有导致深度学习算法的准确性下降。即使高稀疏性导致较低的精度，但统一的分布对于在数据挖掘方面取得可靠的结果至关重要。
Deep learning algorithms vary depending on the underlying connection mechanism of nodes of them. They have various hyperparameters that are either set via specific algorithms or randomly chosen. Meanwhile, hyperparameters of deep learning algorithms have the potential to help enhance the performance of the machine learning tasks. In this paper, a tuning guideline is provided for researchers who cope with issues originated from hyperparameters of deep learning models. To that end, four types of deep learning algorithms are investigated in terms of tuning and data mining perspective. Further, common search methods of hyperparameters are evaluated on four deep learning algorithms. Normalization helps increase the performance of classification, according to the results of this study. The number of features has not contributed to the decline in the accuracy of deep learning algorithms. Even though high sparsity results in low accuracy, a uniform distribution is much more crucial to reach reliable results in terms of data mining.
