中文版 | English

在结果中检索

CSpace

浏览/检索结果: 共5条，第1-5条 帮助

已选(0)清除 条数/页：   排序方式：
An Energy-Efficient In-Memory Accelerator for Graph Construction and Updating 期刊论文
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS, 2024, 卷号: 43, 期号: 6, 页码: 1781-1793
作者:  Chen, Mingkai;  Liu, Cheng;  Liang, Shengwen;  He, Lei;  Wang, Ying;  Zhang, Lei;  Li, Huawei;  Li, Xiaowei
收藏  |  浏览/下载：2/0  |  提交时间：2024/12/06
Memory management  Buildings  Bandwidth  Hardware  Social networking (online)  Energy efficiency  Parallel processing  Graph construction  graph updating  near-data processing  nearest neighbors  power gating  stacked memory  
RECOME: A new density-based clustering algorithm using relative KNN kernel density 期刊论文
INFORMATION SCIENCES, 2018, 卷号: 436, 页码: 13-30
作者:  Geng, Yangli-ao;  Li, Qingyong;  Zheng, Rong;  Zhuang, Fuzhen;  He, Ruisi;  Xiong, Naixue
收藏  |  浏览/下载：59/0  |  提交时间：2019/12/10
Density-based clustering  Density estimation  K nearest neighbors  Graph theory  
Multi-label classification by exploiting local positive and negative pairwise label correlation 期刊论文
NEUROCOMPUTING, 2017, 卷号: 257, 页码: 164-174
作者:  Huang, Jun;  Li, Guorong;  Wang, Shuhui;  Xue, Zhe;  Huang, Qingming
收藏  |  浏览/下载：56/0  |  提交时间：2019/12/12
Multi-label classification  k nearest neighbors  Local label correlation  Positive and negative label correlation  
Study on density peaks clustering based on k-nearest neighbors and principal component analysis 期刊论文
KNOWLEDGE-BASED SYSTEMS, 2016, 卷号: 99, 页码: 135-145
作者:  Du, Mingjing;  Ding, Shifei;  Jia, Hongjie
收藏  |  浏览/下载：49/0  |  提交时间：2019/12/13
Data clustering  Density peaks  k Nearest neighbors (KNN)  Principal component analysis (PCA)  
An active learning based TCM-KNN algorithm for supervised network intrusion detection 期刊论文
COMPUTERS & SECURITY, 2007, 卷号: 26, 期号: 7-8, 页码: 459-467
作者:  Li, Yang;  Guo, Li
收藏  |  浏览/下载：44/0  |  提交时间：2019/12/16
network security  intrusion detection  TCM-KNN (Transductive Confidence Machines for K-Nearest Neighbors)  algorithm  machine learning  active learning