Multiple Instance Support Vector Machines With Latent Variable Description

被引：0
|
作者：
Lu, Lianjiang [1 ]
Li, Wei [1 ]
Wang, Liabao [1 ]
Zhang, Yafei [1 ]
Li, Yang [1 ]
Bao, Lei [1 ]
机构：
[1] PLA Univ Sci & Technol, Coll Command Informat Syst, Nanjing, Jiangsu, Peoples R China
来源：
2013 10TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD) | 2013年
关键词：
Multiple instance learning; Support vector machines; Latent variable models; Stochastic gradient descent;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the latent variable model is adopted to re-describe MI-SVM and its feature mapping variants. MI-SVM with latent variable description and the corresponding stochastic optimization learning algorithm are proposed. In the Musk and Corel datasets, the proposed algorithm achieves higher predicting accuracy and faster learning speed, with strong stability and robustness for parameters and noise.
引用
收藏
页码：433 / 438
页数：6
相关论文
共 50 条
  • [11] A Competitive Learning Approach to Instance Selection for Support Vector Machines
    Zechner, Mario
    Granitzer, Michael
    KNOWLEDGE SCIENCE, ENGINEERING AND MANAGEMENT, 2009, 5914 : 146 - +
  • [12] Fast instance selection for speeding up support vector machines
    Chen, Jingnian
    Zhang, Caiming
    Xue, Xiaoping
    Liu, Cheng-Lin
    KNOWLEDGE-BASED SYSTEMS, 2013, 45 : 1 - 7
  • [13] Purity Filtering: An Instance Selection Method for Support Vector Machines
    Moran-Pomes, David
    Belanche-Munoz, Lluis A.
    ARTIFICIAL INTELLIGENCE XXXVI, 2019, 11927 : 21 - 35
  • [14] Structured variable selection in support vector machines
    Wu, Seongho
    Zou, Hui
    Yuan, Ming
    ELECTRONIC JOURNAL OF STATISTICS, 2008, 2 : 103 - 117
  • [15] A fast instance selection method for support vector machines in building extraction
    Aslani, Mohammad
    Seipel, Stefan
    APPLIED SOFT COMPUTING, 2020, 97
  • [16] Building Sparse Support Vector Machines for Multi-Instance Classification
    Fu, Zhouyu
    Lu, Guojun
    Ting, Kai Ming
    Zhang, Dengsheng
    MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, PT I, 2011, 6911 : 471 - 486
  • [17] Efficient and decision boundary aware instance selection for support vector machines
    Aslani, Mohammad
    Seipel, Stefan
    INFORMATION SCIENCES, 2021, 577 : 579 - 598
  • [18] An information criterion for variable selection in support vector machines
    Claeskens, Gerda
    Croux, Christophe
    Van Kerckhoven, Johan
    JOURNAL OF MACHINE LEARNING RESEARCH, 2008, 9 : 541 - 558
  • [19] Kernel variable selection for multicategory support vector machines
    Park, Beomjin
    Park, Changyi
    JOURNAL OF MULTIVARIATE ANALYSIS, 2021, 186
  • [20] Choosing multiple parameters for support vector machines
    Chapelle, O
    Vapnik, V
    Bousquet, O
    Mukherjee, S
    MACHINE LEARNING, 2002, 46 (1-3) : 131 - 159
12345