CSpace
(本次检索基于用户作品认领结果)

浏览/检索结果: 共9条,第1-9条 帮助

限定条件        
已选(0)清除 条数/页:   排序方式:
A Kalman-Filter-Incorporated Latent Factor Analysis Model for Temporally Dynamic Sparse Data 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2022, 页码: 14
作者:  Yuan, Ye;  Luo, Xin;  Shang, Mingsheng;  Wang, Zidong
收藏  |  浏览/下载:66/0  |  提交时间:2022/08/22
Quality of service  Data models  Kalman filters  Estimation  Computational modeling  Web services  Heuristic algorithms  Alternating least squares (ALSs)  computational intelligence  data science  dynamic latent factor analysis (LFA)  dynamics  intelligent computing  Kalman filter  temporal pattern  Web service  
A Multilayered-and-Randomized Latent Factor Model for High-Dimensional and Sparse Matrices 期刊论文
IEEE TRANSACTIONS ON BIG DATA, 2022, 卷号: 8, 期号: 3, 页码: 784-794
作者:  Yuan, Ye;  He, Qiang;  Luo, Xin;  Shang, Mingsheng
收藏  |  浏览/下载:75/0  |  提交时间:2022/08/22
Computational modeling  Sparse matrices  Big Data  Data models  Stochastic processes  Training  Software algorithms  Big data  latent factor analysis  generally multilayered structure  deep forest  multilayered extreme learning machine  randomized-learning  high-dimensional and sparse matrix  stochastic gradient descent  randomized model  
Elastic-net regularized latent factor analysis-based models for recommender systems 期刊论文
NEUROCOMPUTING, 2019, 卷号: 329, 页码: 66-74
作者:  Wang, Dexian;  Chen, Yanbin;  Guo, Junxiao;  Shi, Xiaoyu;  He, Chunlin;  Luo, Xin;  Yuan, Huaqiang
Adobe PDF(1965Kb)  |  收藏  |  浏览/下载:213/0  |  提交时间:2019/01/17
Big data  Recommender systems  Collaborative filtering  Latent factor analysis  Elastic-net  Regularization  Latent factor distribution  
Wavelet Denoising Algorithm Based on NDOA Compressed Sensing for Fluorescence Image of Microarray 期刊论文
IEEE ACCESS, 2019, 卷号: 7, 页码: 13338-13346
作者:  Gan, Zhenhua;  Zou, Fumin;  Zeng, Nianyin;  Xiong, Baoping;  Liao, Lyuchao;  Li, Han;  Luo, Xin;  Du, Min
Adobe PDF(5435Kb)  |  收藏  |  浏览/下载:160/0  |  提交时间:2019/03/25
Compressed sensing  wavelet denoising  DNA microarray  image filtering  NDOA  
Modified Primal-Dual Neural Networks for Motion Control of Redundant Manipulators With Dynamic Rejection of Harmonic Noises 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 10, 页码: 4791-4801
作者:  Li, Shuai;  Zhou, MengChu;  Luo, Xin
Adobe PDF(2696Kb)  |  收藏  |  浏览/下载:293/0  |  提交时间:2018/11/01
Dual neural network  kinematic control  redundancy resolution  robotic manipulator  
Modified Primal-Dual Neural Networks for Motion Control of Redundant Manipulators With Dynamic Rejection of Harmonic Noises 期刊论文
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 卷号: 29, 期号: 10, 页码: 4791-4801
作者:  Li, Shuai;  Zhou, MengChu;  Luo, Xin
Adobe PDF(2696Kb)  |  收藏  |  浏览/下载:289/0  |  提交时间:2019/06/26
Dual neural network  kinematic control  redundancy resolution  robotic manipulator  
Randomized latent factor model for high-dimensional and sparse matrices from industrial applications 会议论文
15th IEEE International Conference on Networking, Sensing and Control, ICNSC 2018, Zhuhai, China, March 27, 2018 - March 29, 2018
作者:  Chen, Jia;  Luo, Xin
Adobe PDF(5259Kb)  |  收藏  |  浏览/下载:131/0  |  提交时间:2019/06/25
Symmetric and Nonnegative Latent Factor Models for Undirected, High-Dimensional, and Sparse Networks in Industrial Applications 期刊论文
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS, 2017, 卷号: 13, 期号: 6, 页码: 3098-3107
作者:  Luo, Xin;  Sun, Jianpei;  Wang, Zidong;  Li, Shuai;  Shang, Mingsheng
Adobe PDF(803Kb)  |  收藏  |  浏览/下载:428/0  |  提交时间:2018/03/05
Big data application  high-dimensional, and sparse (SHiDS) matrix  nonnegative latent factor (NLF) model  symmetry  undirected HiDS network  
Highly Efficient Framework for Predicting Interactions Between Proteins 期刊论文
IEEE TRANSACTIONS ON CYBERNETICS, 2017, 卷号: 47, 期号: 3, 页码: 731-743
作者:  You, Zhu-Hong;  Zhou, MengChu;  Luo, Xin;  Li, Shuai
Adobe PDF(1407Kb)  |  收藏  |  浏览/下载:221/0  |  提交时间:2018/03/15
Big data  feature extraction  kernel extreme learning machine (K-ELM)  low-rank approximation (LRA)  protein-protein interactions (PPIs)  support vector machine (SVM)