具有深度表示的水平和垂直包围用于分类
Horizontal and Vertical Ensemble with Deep Representation for Classification
摘要：
表示学习，特别是利用深度学习的表示学习，在分类中得到了广泛的应用。然而，如何使用有限大小的标记数据来实现深度神经网络的良好分类性能，以及学习的特征如何进一步改善分类仍然是不确定的。在本文中，我们提出了水平投票垂直投票和水平堆叠包围方法，以提高深度神经网络的分类性能。在ICML 2013黑盒挑战赛中，徐兵通过独立使用这些方法，获得了公开排行榜第3名，私人排行榜第7名;谢晶晶获得了公开排行榜第4名，私人排行榜第5名。
英文摘要：
Representation learning, especially which by using deep learning, has been widely applied in classification. However, how to use limited size of labeled data to achieve good classification performance with deep neural network, and how can the learned features further improve classification remain indefinite. In this paper, we propose Horizontal Voting Vertical Voting and Horizontal Stacked Ensemble methods to improve the classification performance of deep neural networks. In the ICML 2013 Black Box Challenge, via using these methods independently, Bing Xu achieved 3rd in public leaderboard, and 7th in private leaderboard; Jingjing Xie achieved 4th in public leaderboard, and 5th in private leaderboard.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
