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浏览/检索结果: 共17条，第1-10条 帮助 限定条件 内容类型：会议论文    专题：湖南大学    第一署名单位    第一作者单位    通讯作者单位     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 ESNMF: Evolutionary Symmetric Nonnegative Matrix Factorization for Dissecting Dynamic Microbial Networks 会议论文 Intelligent Computing Methodologies. 14th International Conference, ICIC 2018, Wuhan, China, 2018 作者:  Yuanyuan Ma;  Xiaohua Hu;  Tingting He;  Xianchao Zhu;  Meijun Zhou 收藏  |  浏览/下载：10/0  |  提交时间：2019/12/25 Chemical composition of outdoor and indoor PM2.5 collected during haze events: Transformations and modified source contributions resulting from outdoor-to-indoor transport 会议论文 INDOOR AIR, 828-839, 2018 作者:  Xie, Yangyang;  Zhao, Bin 收藏  |  浏览/下载：12/0  |  提交时间：2019/12/25 air  infiltration  haze  indoor  air  PM2.5  positive  matrix  factorization  source  apportionment   A Multi-Factor Influencing POI Recommendation Model Based on Matrix Factorization 会议论文 10th International Conference on Advanced Computational Intelligence (ICACI), xiamen, PEOPLES R CHINA, 2018 作者:  Xu, Y;  Li, Y;  Yang, W;  Zhang, J;  IEEE 收藏  |  浏览/下载：3/0  |  提交时间：2019/12/25 recommendation system  point of interest (POI)  matrix factorization  LBSN   Predicting Microbe-Disease Association by Kernelized Bayesian Matrix Factorization 会议论文 Intelligent Computing Theories and Application. 14th International Conference, ICIC 2018, Wuhan, China, 2018 作者:  Sisi Chen;  Dan Liu;  Jia Zheng;  Pingtao Chen;  Xiaohua Hu 收藏  |  浏览/下载：1/0  |  提交时间：2019/12/25 CuSNMF: A sparse non-negative matrix factorization approach for large-scale collaborative filtering recommender systems on multi-GPU 会议论文 15th IEEE International Symposium on Parallel and Distributed Processing with Applications and 16th IEEE International Conference on Ubiquitous Computing and Communications, ISPA/IUCC 2017, Guangzhou, China, 2017 作者:  Li, Hao;  Li, Kenli;  Peng, Jiwu;  Li, Keqin 收藏  |  浏览/下载：1/0  |  提交时间：2019/12/26 Improving Matrix Factorization Recommendations for Problems in Big Data 会议论文 2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）, 中国北京, 2017 作者:  Xiaohan Tu;  Siping Liu;  Renfa Li 收藏  |  浏览/下载：0/0  |  提交时间：2019/12/26 An efficient parallelization approach for large-scale sparse non-negative matrix factorization using kullback-leibler divergence on Multi-GPU 会议论文 15th IEEE International Symposium on Parallel and Distributed Processing with Applications and 16th IEEE International Conference on Ubiquitous Computing and Communications, ISPA/IUCC 2017, Guangzhou, China, 2017 作者:  Li, Hao;  Li, Kenli;  Peng, Jiwu;  Hu, Junyan;  Li, Keqin 收藏  |  浏览/下载：0/0  |  提交时间：2019/12/26 An Efficient Parallelization Approach for Large-scale Sparse Non-negative Matrix Factorization Using Kullback-Leibler Divergence on Multi-GPU 会议论文 15th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) / 16th IEEE International Conference on Ubiquitous Computing and Communications (IUCC), Guangzhou, PEOPLES R CHINA, 2017 作者:  Li, H;  Li, KL;  Peng, JW;  Hu, JY;  Li, KQ 收藏  |  浏览/下载：1/0  |  提交时间：2019/12/26 Collaborative filtering (CF)  CUDA parallelization approach  Multi-GPU  Non-negative Matrix Factorization (NMF)  Recommender systems  L-2-norm regularization   CuSNMF: A Sparse Non-negative Matrix Factorization Approach for Large-Scale Collaborative Filtering Recommender Systems on Multi-GPU 会议论文 15th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA) / 16th IEEE International Conference on Ubiquitous Computing and Communications (IUCC), Guangzhou, PEOPLES R CHINA, 2017 作者:  Li, H;  Li, KL;  Peng, JW;  Li, KQ 收藏  |  浏览/下载：1/0  |  提交时间：2019/12/26 Collaborative filtering (CF)  CUDA parallelization approach  Multi-GPU  Non-negative Matrix Factorization (NMF)  Recommender systems  L-2-norm regularization   Improving Matrix Factorization Recommendations for Problems in Big Data 会议论文 2017 IEEE 2ND INTERNATIONAL CONFERENCE ON BIG DATA ANALYSIS (ICBDA), 198-202, 2017 作者:  Tu, XH;  Liu, SP;  Li, RF;  IEEE 收藏  |  浏览/下载：1/0  |  提交时间：2019/12/26 component  recommender systems  data sparsity problem  collaborative filtering  alternating-least-squares with Weighted- lambda -regularization (ALS-WR)