合作者关系

[中文学位论文] 多维标度在图像排序中的应用

作者：于盼盼

学位名称：硕士

出处：北京理工大学 2017

[外文期刊] An Efficient Method for Sample Adversarial Perturbations against Nonlinear Support Vector Machines EI

作者：Su, Wen;Li, Qingna （¹School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, China;²School of Mathematics and Statistics, Beijing Key Laboratory on MCAACI, Key Laboratory of Mathematical Theory and Computation in Information Security, Beijing Institute of Technology, Beijing, China）

出处：arXiv 2022

摘要：Adversarial perturbations have drawn great attentions in various machine learning models. In this paper, we investigate the sample adversarial perturb ...

[外文期刊] A facial reduction approach for the single source localization problem SCIE EI

作者：He Shi¹; & Qingna Li ²; （¹School of Mathematics and Statistics, Beijing Institute of Technology, Beijing, 100081, China;²School of Mathematics and Statistics, Beijing Institute of Technology/Key Laboratory of Mathematical Theory and Computation in Information Security, Beijing, 100081, China）

出处：Journal of Global Optimization 2022

关键词：Single source localization;Euclidean distance matrix;Facial reduction;Majorized penalty approach;Constraint nondegeneracy

摘要：The single source localization problem (SSLP) appears in several fields such as signal processing and global positioning systems. The optimization pro ...

[外文期刊] A Facial Reduction Approach for the Single Source Localization Problem

作者：Shi, He;Li, Qingna （¹School of Mathematics and Statistics, Beijing Institute of Technology, Beijing; 100081, China;²School of Mathematics and Statistics, Beijing Key Laboratory on MCAACI, Key Laboratory of Mathematical Theory and Computation in Information Security, Beijing Institute of Technology, Beijing; 100081, China）

出处：arXiv 2021

摘要：The single source localization problem (SSLP) appears in several fields such as signal processing and global positioning systems. The optimization pro ...

[中文期刊] 中国乳腺癌自然史参数选择的优化方法核

作者：尹娟¹;，王乐^2,3;，白晓宁⁴;，李燕婕²;，王鑫²;，张在坤⁵;，李炳照⁴;，李扬⁶;，石菊芳²;，李庆娜⁴; （¹北京理工大学管理与经济学院；²国家癌症中心国家肿瘤临床医学研究中心和中国医学科学院北京协和医学院肿瘤医院癌症早诊早治办公室；³浙江省肿瘤医院；⁴北京理工大学数学与统计学院MCAACI北京市重点实验室和信息安全的数学理论与计算工信部重点实验室；⁵香港理工大学应用数学系；⁶中国医学科学院北京协和医学院医学信息研究所）

出处：中国科学(数学) 2023

关键词：乳腺癌；自然史；参数选择；黄金分割法；无导数法；坐标下降法

摘要：乳腺癌是女性最常见的恶性肿瘤之一.为了提出有效的筛查策略并评估其效果,一个基本且重要的步骤是在中国乳腺癌自然史模型中选择合适的参数,即转移概率.选择合理的转移概率有两个挑战.首先,由于乳腺癌的流行病学特性,其他国家使用的转移概率不一定适用于中国.其次,可用的筛查样本数据很少,这使得传统的基于统计的方 ...

[外文期刊] Optimization Models and Interpretations for Three Types of Adversarial Perturbations against Support Vector Machines EI

作者：Su, Wen¹; Li, Qingna²; Cui, Chunfeng³; （¹School of Mathematics and Statistics, Beijing Institute of Technology, Beijing; 100081, China;²School of Mathematics and Statistics, Beijing Key Laboratory on MCAACI, Key Laboratory of Mathematical Theory and Computation in Information Security, Beijing Institute of Technology, Beijing; 100081, China;³The Ministry of Education, School of Mathematical Sciences, Beihang University, 100191, China）

出处：arXiv 2022

摘要：Adversarial perturbations have drawn great attentions in various deep neural networks. Most of them are computed by iterations and cannot be interpret ...

[外文期刊] A Highly Efficient Adaptive-Sieving-Based Algorithm for the High-Dimensional Rank Lasso Problem EI

作者：Bai, Xiaoning¹;Li, Qingna²; （¹School of Mathematics and Statistics, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Haidian District, Beijing; 100081, China;²School of Mathematics and Statistics, Beijing Key Laboratory on MCAACI, Key Laboratory of Mathematical Theory and Computation in Information Security, Beijing Institute of Technology, No. 5 Zhongguancun South Street, Haidian District, Beijing; 100081, China）

出处：arXiv 2022

摘要：The high-dimensional rank lasso (hdr lasso) model is an efficient approach to deal with high-dimensional data analysis. It was proposed as a tuning-fr ...