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	Early Recognition of Sclerotinia Stem Rot on Oilseed Rape by Hyperspectral Imaging Combined With Deep Learning 期刊论文 OAI收割 SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 卷号: 43, 期号: 7, 页码: 2220-2225 作者: Liang Wan-jie; Feng Hui; Jiang Dong; Zhang Wen-yu; Cao Jing \| 收藏 \| 浏览/下载：16/0 \| 提交时间：2023/10/09 Deep convolution neural network Hyperspectral imaging Sclerotinia stem rot on oilseed rape Early recognition Resnet50
	A new early stopping algorithm for improving neural network generalization (EI CONFERENCE) 会议论文 OAI收割 2009 2nd International Conference on Intelligent Computing Technology and Automation, ICICTA 2009, October 10, 2009 - October 11, 2009, Changsha, Hunan, China 作者: Liu J.-G.; Wu X.-X. 收藏 \| 浏览/下载：23/0 \| 提交时间：2013/03/25 As generalization ability of neural network was restricted by overfitting problem in the network's training. Early stopping algorithm based on fuzzy clustering was put forward to solve this problem in this paper. Subtractive clustering and Fuzzy C-Means clustering (FCM) were combined to realize optimal division of training set validation set and test set. How to realize this algorithm in backpropagation (BP) network by utilizing neural network toolbox and fuzzy logic toolbox in MATLAB was dwelled on. Early stopping algorithm based on fuzzy clustering and other early stopping algorithms were applied in function approximation and pattern recognition problems in validation experiments. Experiments results indicate that early stopping algorithm based on fuzzy clustering has higher precision in comparison to other early stopping algorithms. Outputs of training set validation set and test set are more accordant. 2009 IEEE.
	Contour extracting with combination particle filtering and em algorithm (EI CONFERENCE) 会议论文 OAI收割 International Symposium on Photoelectronic Detection and Imaging, ISPDI 2007: Related Technologies and Applications, September 9, 2007 - September 12, 2007, Beijing, China Meng B.; Zhu M. 收藏 \| 浏览/下载：28/0 \| 提交时间：2013/03/25 The problem of extracting continuous structures from images is a difficult issue in early pattern recognition and image processings[1]. Tracking with contours in a filtering framework requires a dynamical model for prediction. Recently Particle filter is widely used because its multiple hypotheses and versatility within framework. However the good choice of the propagation function is still its main problem. In this paper an improved particle filter EM-PF algorithm is proposed which using the EM (Expectation-Maximization) algorithm to learn the dynamical models. The EM algorithm can explicitly learn the parameters of the dynamical models from training sequences. The advantage of using the EM algorithm in particle filter is that it is capable of improve tracking contour by having accurate model parameters. Though the experiment results we show how our EM-PF can be applied to produces more robust and accurate extracting.