QQ客服 官方微博 反馈留言

	Dynamic boosting in deep learning using reconstruction error
	Huang, Wenhao ; Zhang, Ni ; Hu, Weisong ; Hong, Haikun ; Song, Guojie ; Xie, Kunqing
	2014
英文摘要	Deep learning has attracted a lot of attention in research and industry in recent years. Behind the success of deep learning, there is much space for improvement. It is difficult to identify if a testing sample can be represented by the deep network effectively before we examining the final result. In this paper, we proposed a dynamic boosting strategy according to reconstruction error in deep networks. We use reconstruction error to determine whether the result is reliable or not. From the perspective of prediction interval, we demonstrated that with the increase of reconstruction error, the prediction interval would become bigger. Therefore, the classification result is not reliable when the reconstruction error exceeds the predetermined threshold. Since we can record the reconstruction error as well as the classification error for all training samples in training set. We can learn an extra boosting model besides the deep network in training set to improve the performance of the model. An important factor in learning the boosting model is to determine an appropriate threshold for selecting training samples. In testing, we first examine whether the reconstruction error of a testing sample exceeds the threshold to determine if we should use the boosting model. If the boosting model is used, the final result is the average of the output of the deep network and the boosting model. We conducted experiments on two widely used classification datasets and an air quality dataset. From the experiments, we see that our boosting strategy is effective in improving the performance of classification. We tested several boosting models in this paper. They can all reduce the test error to some extent under appropriate parameter settings. ? 2014 IEEE.; EI; 473-480
语种	英语
出处	2014 International Joint Conference on Neural Networks, IJCNN 2014
DOI标识	10.1109/IJCNN.2014.6889534
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/424017]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Huang, Wenhao,Zhang, Ni,Hu, Weisong,et al. Dynamic boosting in deep learning using reconstruction error. 2014-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。