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兰州理工大学 [14]

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	Deep InterBoost networks for small-sample image classification 期刊论文 NEUROCOMPUTING, 2021, 卷号: 456, 页码: 492-503 作者: Li, Xiaoxu; Chang, Dongliang; Ma, Zhanyu; Tan, Zheng-Hua; Xue, Jing-Hao 收藏 \| 浏览/下载：6/0 \| 提交时间：2021/10/14 Ensemble learning Deep neural network Small-sample image classification Overfitting
	FewHshot image classification method based on sliding feature vectors 期刊论文 Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition), 2021, 卷号: 51, 期号: 5, 页码: 1785-1791 作者: Cao, Jie; Qu, Xue; Li, Xiao-Xu 收藏 \| 浏览/下载：15/0 \| 提交时间：2022/02/17 Query processing Vector spaces Class distributions Classification methods Computer applications technologies Feature space Features vector Few-shot learning Images classification Local feature Metric learning Misclassifications
	Research on Classification of COVID-19 Chest X-Ray Image Modal Feature Fusion Based on Deep Learning 期刊论文 JOURNAL OF HEALTHCARE ENGINEERING, 2021, 卷号: 2021 作者: Ji, Dongsheng; Zhang, Zhujun; Zhao, Yanzhong; Zhao, Qianchuan 收藏 \| 浏览/下载：18/0 \| 提交时间：2021/10/14
	Remarnet: Conjoint relation and margin learning for small-sample image classification 期刊论文 IEEE Transactions on Circuits and Systems for Video Technology, 2021, 卷号: 31, 期号: 4, 页码: 1569-1579 作者: Li, Xiaoxu; Yu, Liyun; Yang, Xiaochen; Ma, Zhanyu; Xue, Jing-Hao 收藏 \| 浏览/下载：8/0 \| 提交时间：2021/06/03 Deep learning Image classification Sampling Classification mechanism Competitive performance Discriminative features Discriminative power Fully connected networks Novel neural network State-of-the-art methods State-of-the-art performance
	ReMarNet: Conjoint Relation and Margin Learning for Small-Sample Image Classification 期刊论文 IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2021, 卷号: 31, 期号: 4, 页码: 1569-1579 作者: Li, Xiaoxu; Yu, Liyun; Yang, Xiaochen; Ma, Zhanyu; Xue, Jing-Hao 收藏 \| 浏览/下载：6/0 \| 提交时间：2021/06/03 Training Prototypes Neural networks Feature extraction Task analysis Deep learning Adaptation models Small-sample learning deep neural network relation learning discriminative feature learning
	Short-Term Traffic Flow Prediction Based on LSTM-XGBoost Combination Model 期刊论文 CMES - Computer Modeling in Engineering and Sciences, 2020, 卷号: 125, 期号: 1, 页码: 95-109 作者: Zhang XJ(张玺君) 收藏 \| 浏览/下载：11/0 \| 提交时间：2020/11/13 Forecasting Long short-term memory Predictive analytics Roads and streets Time series
	OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer 会议论文作者: Li, Xiaoxu; Chang, Dongliang; Ma, Zhanyu; Tan, Zheng-Hua; Xue, Jing-Hao 收藏 \| 浏览/下载：2/0 \| 提交时间：2020/12/18 Benchmarking Deep neural networks Large dataset TestingBenchmark datasets Discriminative features Function spaces Generalization error bounds Nonlinear layers Number of class Rademacher complexity Small sample datum
	OSLNet: Deep Small-Sample Classification with an Orthogonal Softmax Layer 期刊论文 IEEE Transactions on Image Processing, 2020, 卷号: 29, 页码: 6482-6495 作者: Li, Xiaoxu; Chang, Dongliang; Ma, Zhanyu; Tan, Zheng-Hua; Xue, Jing-Hao 收藏 \| 浏览/下载：2/0 \| 提交时间：2020/11/14 Benchmarking Deep neural networks Large dataset Testing Benchmark datasets Discriminative features Function spaces Generalization error bounds Nonlinear layers Number of class Rademacher complexity Small sample datum
	Small-sample image classification method of combining prototype and margin learning 会议论文 Lanzhou, China, November 18, 2019 - November 21, 2019 作者: Li, Xiaoxu; Yu, Liyun; Cao, Jie; Chang, Dongliang; Ma, Zhanyu 收藏 \| 浏览/下载：0/0 \| 提交时间：2020/11/15 Classification (of information) Deep neural networks Image classification Large dataset Learning systems Neural networks Classification methods Ensemble learning Ensemble methods Large-scale datasets Prototype and margin learning Prototype learning Small sample datum Small samples
	Yinger learning dynamic fuzzy neural network algorithm for the three stage inverted pendulum 会议论文 Guangzhou, China, August 25, 2015 - August 26, 2015 作者: Zhang, Ping; Gao, Guodong; Zhang, Xin; Chen, Wei 收藏 \| 浏览/下载：12/0 \| 提交时间：2020/11/15 Fuzzy inference Fuzzy logic Learning algorithms Pendulums Dynamic fuzzy neural networks(D-FNN) Inverted pendulum Inverted pendulum system Knowledge extraction Lazy learning Overfitting

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