机构

• 数学与系统科学研究院 [4]
• 计算技术研究所 [2]
• 长春光学精密机械与物... [1]
• 国家天文台 [1]
• 自动化研究所 [1]
• 西安光学精密机械研究... [1]
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采集方式

• OAI收割 [10]

内容类型

• 期刊论文 [8]
• 会议论文 [1]
• 学位论文 [1]

发表日期

• 2025 [2]
• 2024 [1]
• 2021 [1]
• 2019 [1]
• 2016 [1]
• 2015 [1]
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学科主题

筛选

浏览/检索结果: 共10条，第1-10条 帮助 条数/页： 5101520253035404550556065707580859095100 排序方式： 请选择发表日期升序发表日期降序题名升序题名降序提交时间升序提交时间降序作者升序作者降序 SSC-PPI: A Subspace Structure Consistency-Based Method for Protein-Protein Interactions Prediction 期刊论文  OAI收割 IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 2025, 卷号: 22, 期号: 6, 页码: 2477-2490 作者:  Ma, Ziping;  Min, Weiqing;  Zhang, Huanpu;  Huang, Yulei;  Jiang, Shuqiang   |  收藏  |  浏览/下载：5/0  |  提交时间：2026/05/25 Proteins  Feature extraction  Drugs  Diseases  Amino acids  Accuracy  Predictive models  Dimensionality reduction  Data mining  Databases  Protein-protein interactions  dimension reduction  unsupervised feature selection  latent representation learning   AdaE: Knowledge Graph Embedding With Adaptive Embedding Sizes 期刊论文  OAI收割 IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 2025, 卷号: 37, 期号: 8, 页码: 4432-4445 作者:  Guan, Zhanpeng;  Zhang, Fuwei;  Zhang, Zhao;  Zhuang, Fuzhen;  Wang, Fei   |  收藏  |  浏览/下载：1/0  |  提交时间：2025/12/03 Knowledge graphs  Adaptation models  Training  Data models  Search problems  Vectors  Overfitting  Tail  Optimization  Tensors  Knowledge graph embedding (KGE)  Data imbalance issue  Dimension selection   Scale and pattern adaptive local binary pattern for texture classification[Formula presented] 期刊论文  OAI收割 Expert Systems with Applications, 2024, 卷号: 240 作者:  Hu, Shiqi;  Li, Jie;  Fan, Hongcheng;  Lan, Shaokun;  Pan, Zhibin   |  收藏  |  浏览/下载：97/0  |  提交时间：2024/02/07 Local binary pattern (LBP)  Texture classification  Low dimension  Scale and pattern adaptive selection  Kirsch operator   Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model 期刊论文  OAI收割 JOURNAL OF MACHINE LEARNING RESEARCH, 2021, 卷号: 22, 页码: 39 作者:  Hao, Meiling;  Qu, Lianqiang;  Kong, Dehan;  Sun, Liuquan;  Zhu, Hongtu   |  收藏  |  浏览/下载：80/0  |  提交时间：2021/10/26 High dimension  Imaging genetics  Matrix linear regression  Optimal mini-max rate  Variable selection   Parameter Estimation and Variable Selection for Big Systems of Linear Ordinary Differential Equations: A Matrix-Based Approach 期刊论文  OAI收割 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION, 2019, 卷号: 114, 期号: 526, 页码: 657-667 作者:  Wu, Leqin;  Qiu, Xing;  Yuan, Ya-xiang;  Wu, Hulin   |  收藏  |  浏览/下载：86/0  |  提交时间：2020/01/10 Complex system  Eigenvalue updating algorithm  High dimension  Matrix-based variable selection  Ordinary differential equation  Separable least squares   Dimension reduction based linear surrogate variable approach for model free variable selection 期刊论文  OAI收割 JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2016, 卷号: 169, 页码: 13-26 作者:  Dai, Pengjie;  Ding, Xiaobo;  Wang, Qihua   |  收藏  |  浏览/下载：44/0  |  提交时间：2018/07/30 Adaptive LASSO  Central subspace  Linear surrogate variable  Sufficient dimension reduction  Variable selection   Model selection and estimation in high dimensional regression models with group SCAD 期刊论文  OAI收割 STATISTICS & PROBABILITY LETTERS, 2015, 卷号: 103, 页码: 86-92 作者:  Guo, Xiao;  Zhang, Hai;  Wang, Yao;  Wu, Jiang-Lun   |  收藏  |  浏览/下载：42/0  |  提交时间：2018/07/30 Group selection  High dimension  Oracle property  Group SCAD  Sparsity   高光谱影像空间-光谱特征选择与提取方法研究 学位论文  OAI收割 工学博士, 中国科学院自动化研究所: 中国科学院大学, 2014 作者:  张骞 收藏  |  浏览/下载：855/0  |  提交时间：2015/09/02 高光谱  特征降维  流形学习  稀疏学习  特征选择  特征提取  影像分类  Hyperspectral  Dimension Reduction  Manifold Learning  Sparse Learning  Feature Selection  Feature Extraction  Image Classification   Variable selection in high-dimensional partially linear additive models for composite quantile regression 期刊论文  OAI收割 COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2013, 卷号: 65, 页码: 56-67 作者:  Guo, Jie;  Tang, Manlai;  Tian, Maozai;  Zhu, Kai 收藏  |  浏览/下载：37/0  |  提交时间：2016/11/17 Adaptive Lasso  Composite quantile regression  High-dimension  Semiparametric additive partial linear model  Spline approximation  Variable selection   Using bidirectional binary particle swarm optimization for feature selection in feature-level fusion recognition system (EI CONFERENCE) 会议论文  OAI收割 2009 4th IEEE Conference on Industrial Electronics and Applications, ICIEA 2009, May 25, 2009 - May 27, 2009, Xi'an, China 作者:  Wang D.;  Wang Y.;  Wang Y.;  Wang Y.;  Wang Y. 收藏  |  浏览/下载：43/0  |  提交时间：2013/03/25 In feature-level fusion recognition system  the other is optimizing system sensor design to get outstanding cost performance. So feature selection become usually necessary to reduce dimensionality of the combination of multi-sensor features and improve system performance in system design. In general  there are two main missions. One is improving the recognition correct rate as soon as possible  the optimization is usually applied to feature selection because of its computational feasibility and validity. For further improving recognition accuracy and reducing selected feature dimensions  this paper presents a more rational and accurate optimization  Bidirectional Binary Particle Swarm Optimization (BBPSO) algorithm for feature selection in feature-level fusion target recognition system. In addition  we introduce a new evaluating function as criterion function in BBPSO feature selection method. At the last  we utilized Leave-One-Out method to validate the proposed method. The experiment results show that the proposed algorithm improves classification accuracy by two percentage points  while the selected feature dimensions are less one dimension than original Particle Swarm Optimization approach with 16 original feature dimensions. 2009 IEEE.