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检索条件"主题词=DC Algorithm"
32 条 记 录,以下是11-20 订阅
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Visualizing data as objects by dc (difference of convex) optimization
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MATHEMATICAL PROGRAMMING 2018年 第1期169卷 119-140页
作者: Carrizosa, Emilio Guerrero, Vanesa Morales, Dolores Romero Univ Seville IMUS Inst Matemat Seville Spain Copenhagen Business Sch Frederiksberg Denmark
In this paper we address the problem of visualizing in a bounded region a set of individuals, which has attached a dissimilarity measure and a statistical value, as convex objects. This problem, which extends the stan... 详细信息
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dc programming for sparse proximal support vector machines
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INFORMATION SCIENCES 2021年 547卷 187-201页
作者: Li, Guoquan Yang, Linxi Wu, Zhiyou Wu, Changzhi Chongqing Normal Univ Sch Math Sci Chongqing 401331 Peoples R China Guangzhou Univ Sch Management Guangzhou 510006 Peoples R China
Proximal support vector machine (PSVM), as a variant of support vector machine (SVM), is to generate a pair of non-parallel hyperplanes for classification. Although PSVM is one of the powerful classification tools, it... 详细信息
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Solution methodologies for minimizing a sum of pointwise minima of two functions
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OPTIMIZATION LETTERS 2023年 第1期17卷 75-87页
作者: Zuo, Xinyi Jiang, Yi Sichuan Normal Univ Sch Math Sci Visual Comp & Virtual Real Key Lab Chengdu 610066 Sichuan Peoples R China
In this paper, an NP-hard problem of minimizing a sum of pointwise minima of two functions is considered. Using a new equivalent formula, we propose a smooth approximation and an ADMM algorithm for solving the problem... 详细信息
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Exact recovery low-rank matrix via transformed affine matrix rank minimization
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NEUROCOMPUTING 2018年 319卷 1-12页
作者: Cui, Angang Peng, Jigen Li, Haiyang Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China Guangzhou Univ Sch Math & Informat Sci Guangzhou 510006 Guangdong Peoples R China Xian Polytech Univ Sch Sci Xian 710048 Shaanxi Peoples R China
The goal of affine matrix rank minimization problem is to reconstruct a low-rank or approximately low-rank matrix under linear constraints. In general, this problem is combinatorial and NP-hard. In this paper, a nonco... 详细信息
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Robust truncated L2-norm twin support vector machine
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INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2021年 第12期12卷 3415-3436页
作者: Yang, Linxi Li, Guoquan Wu, Zhiyou Wu, Changzhi Chongqing Normal Univ Dept Math Sci Chongqing 401331 Peoples R China Chongqing Ctr Appl Math Chongqing 401331 Peoples R China Guangzhou Univ Sch Management Guangzhou 510006 Peoples R China
This paper proposes a new robust truncated L-2-norm twin support vector machine ((TSVM)-S-2), where the truncated L-2-norm is used to measure the empirical risk to make the classifiers more robust when encountering lo... 详细信息
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Smoothing techniques and difference of convex functions algorithms for image reconstructions
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OPTIMIZATION 2020年 第7-8期69卷 1601-1633页
作者: Nguyen Mau Nam Le Thi Hoai An Giles, Daniel Nguyen Thai An Portland State Univ Farlborz Maseeh Dept Math & Stat Portland OR 97207 USA Univ Lorraine Comp Sci & Applicat Dept LGIPM Metz France Santa Barbara City Coll Dept Math Santa Barbara CA USA Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu Sichuan Peoples R China
In this paper, we study characterizations of differentiability for real-valued functions based on generalized differentiation. These characterizations provide the mathematical foundation for Nesterov's smoothing t... 详细信息
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Personalized Dose Finding Using Outcome Weighted Learning
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JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2016年 第516期111卷 1509-1521页
作者: Chen, Guanhua Zeng, Donglin Kosorok, Michael R. Vanderbilt Univ Dept Biostat 221 Kirkland Hall Nashville TN 37235 USA Univ N Carolina Dept Biostat Chapel Hill NC 27599 USA Univ N Carolina Dept Stat Chapel Hill NC USA
In dose-finding clinical trials, it is becoming increasingly important to account for individual-level heterogeneity while searching for optimal doses to ensure an optimal individualized dose rule (IDR) maximizes the ... 详细信息
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Linear convergence of a type of iterative sequences in nonconvex quadratic programming
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JOURNAL OF MATHEMATICAL ANALYSIS AND APPLICATIONS 2015年 第2期423卷 1311-1319页
作者: Hoang Ngoc Tuan Hanoi Pedag Univ Dept Math Phuc Yen Vinh Phuc Vietnam
By using error bounds for affine variational inequalities we prove that any iterative sequence generated by the Projection dc (Difference-of-Convex functions) decomposition algorithm in quadratic programming is R-line... 详细信息
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Maximum-Margin Model for Nearest Prototype Classifiers
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JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS 2018年 第4期22卷 565-577页
作者: Kusunoki, Yoshifumi Wakou, Chiharu Tatsumi, Keiji Osaka Univ Grad Sch Engn 2-1 Yamada Oka Suita Osaka 5650871 Japan
In this paper, we study nearest prototype classifiers, which classify data instances into the classes to which their nearest prototypes belong. We propose a maximum-margin model for nearest prototype classifiers. To p... 详细信息
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Boundedness of a Type of Iterative Sequences in Two-Dimensional Quadratic Programming
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JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS 2015年 第1期164卷 234-245页
作者: Hoang Ngoc Tuan Hanoi Pedag Univ Dept Math Phuc Yen Vinh Phuc Vietnam
We prove that any iterative sequence generated by the projection decomposition algorithm of Pham Dinh et al. (Optim Methods Softw 23:609-629, 2008) in quadratic programming is bounded, provided that the quadratic prog... 详细信息
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