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科研机构

大连理工大学 [4]

山东大学 [3]

北京航空航天大学 [1]

成都山地灾害与环境研... [1]

计算技术研究所 [1]

心理研究所 [1]

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期刊论文 [16]

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2019 [16]

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浏览/检索结果: 共16条，第1-10条

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限定条件	发表日期：2019 内容类型：期刊论文

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	HybridTune: Spatio-Temporal Performance Data Correlation for Performance Diagnosis of Big Data Systems 期刊论文 JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2019, 卷号: 34, 期号: 6, 页码: 1167-1184 作者: Ren, Rui; Cheng, Jiechao; He, Xi-Wen; Wang, Lei; Zhan, Jian-Feng 收藏 \| 浏览/下载：1/0 \| 提交时间：2020/12/10 Big Data system spatio-temporal correlation rule-based diagnosis machine learning
	Which Matters More in Incidental Category Learning: Edge-Based Versus Surface-Based Features 期刊论文 FRONTIERS IN PSYCHOLOGY, 2019, 卷号: 10, 页码: 12 作者: Zhou, Xiaoyan; Fu, Qiufang; Rose, Michael; Sun, Yuqi 收藏 \| 浏览/下载：56/0 \| 提交时间：2019/03/11 category learning incidental category learning the prototype distortion task edge-based features surface-based features
	Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization 期刊论文 JOURNAL OF HYDROLOGY, 2019, 卷号: 576, 页码: 229-238 作者: Feng, Zhong-kai; Niu, Wen-jing; Zhang, Rui; Wang, Sen; Cheng, Chun-tian 收藏 \| 浏览/下载：57/0 \| 提交时间：2019/08/15 Hydropower reservoir Operation rule derivation k-Means clustering Extreme learning machine Particle swarm optimization
	Primary Tumor Site Specificity is Preserved in Patient-Derived Tumor Xenograft Models 期刊论文 FRONTIERS IN GENETICS, 2019, 卷号: 10 作者: Chen, Lei; Pan, Xiaoyong; Zhang, Yu-Hang; Hu, Xiaohua; Feng, KaiYan 收藏 \| 浏览/下载：14/0 \| 提交时间：2019/12/05 Patient-derived tumor xenograft gene expression profile Monte Carlo feature selection support vector machine rule learning algorithm
	Comparison of Multiple Linear Regression, Artificial Neural Network, Extreme Learning Machine, and Support Vector Machine in Deriving Operation Rule of Hydropower Reservoir 期刊论文 WATER, 2019, 卷号: 11 作者: Niu, Wen-Jing; Feng, Zhong-Kai; Feng, Bao-Fei; Min, Yao-Wu; Cheng, Chun-Tian 收藏 \| 浏览/下载：9/0 \| 提交时间：2019/12/02 hydropower reservoir operation rule derivation multiple linear regression artificial neural network extreme learning machine support vector machine dynamic programming
	Stacking-based ensemble learning of decision trees for interpretable prostate cancer detection 期刊论文 APPLIED SOFT COMPUTING, 2019, 卷号: 77, 页码: 188-204 作者: Wang, Yuyan; Wang, Dujuan; Geng, Na; Wang, Yanzhang; Yin, Yunqiang 收藏 \| 浏览/下载：6/0 \| 提交时间：2019/12/02 Prostate cancer detection Ensemble learning Stacking Rule extraction Multi-objective optimization
	Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization 期刊论文 JOURNAL OF HYDROLOGY, 2019, 卷号: 576, 页码: 229-238 作者: Feng, Zhong-kai; Niu, Wen-jing; Zhang, Rui; Wang, Sen; Cheng, Chun-tian 收藏 \| 浏览/下载：5/0 \| 提交时间：2019/12/02 Hydropower reservoir Operation rule derivation k-Means clustering Extreme learning machine Particle swarm optimization
	Dominance rule and opposition-based particle swarm optimization for two-stage assembly scheduling with time cumulated learning effect 期刊论文 SOFT COMPUTING, 2019, 卷号: 23, 页码: 9617-9628 作者: Wang, Dujuan; Qiu, Huaxin; Wu, Chin-Chia; Lin, Win-Chin; Lai, Kunjung 收藏 \| 浏览/下载：8/0 \| 提交时间：2019/12/02 Two-stage assembly Flowshop scheduling Time cumulated learning function Dominance rule Particle swarm optimization
	FKRR-MVSF: A Fuzzy Kernel Ridge Regression Model for Identifying DNA-Binding Proteins by Multi-View Sequence Features via Chou's Five-Step Rule. 期刊论文 International Journal of Molecular Sciences, 2019, 卷号: Vol.20 No.17, 页码: 4175 作者: Zou, Yi; Ding, Yijie; Tang, Jijun; Guo, Fei; Peng, Li 收藏 \| 浏览/下载：8/0 \| 提交时间：2019/11/21 DNA-binding proteins prediction feature extraction fuzzy kernel ridge regression multiple kernel learning protein sequence
	Learning joint space–time–frequency features for EEG decoding on small labeled data 期刊论文 Neural Networks, 2019, 卷号: 114, 页码: 67-77 作者: Feng XS(封锡盛); Si BL(斯白露); Tang FZ(唐凤珍); Zhao DY(赵冬晔) 收藏 \| 浏览/下载：110/0 \| 提交时间：2019/04/13 Brain-computer interfaces Convolutional neural network Joint space–time–frequency feature learning Subject-to-subject weight transfer Small labeled data

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