I)raining support vector machines based on genetic algorithms

被引：0
|
作者：
Yuan, XF [1 ]
Wang, YN [1 ]
机构：
[1] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China
来源：
ICCC2004: PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATION VOL 1AND 2 | 2004年
关键词：
SVM; genetic algorithms; support vectors; reduced vectors; quadratic programming;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
To overcome the vast computation of training SVM, fast training techniques are very useful and valuable. In this paper, we introduce an Improved GetBorder method which selects potential support vectors as reduced vectors. Then training SVM is equivalent to a quadratic programming of reduced vectors, whose size is very smaller comparing with the training data. Finally we apply genetic algorithms to optimize the quadratic programming of reduced vectors. Simulations illustrate that for linear separable training data, this novel method can train SVM effectively and efficiently. This method is also feasible even if the training samples scale is very large.
引用
收藏
页码：1729 / 1735
页数：7
相关论文
共 50 条
  • [21] Ore grade interpolation model based on support vector machines optimized by genetic algorithms
    [J]. Li, C.-P. (cpli@ustb.edu.cn), 1600, University of Science and Technology Beijing (35):
  • [22] Stability recognition of tunnel cavern foundation based on support vector machines with genetic algorithms
    College Civil and Hydraulic Engineering, Dalian University of Technology, Dalian 116024, China
    [J]. Liaoning Gongcheng Jishu Daxue Xuebao (Ziran Kexue Ban), 2007, 5 (703-705):
  • [23] Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms
    Pai, PF
    Hong, WC
    [J]. ELECTRIC POWER SYSTEMS RESEARCH, 2005, 74 (03) : 417 - 425
  • [24] Research on stock price forecasting methods by support vector machines based on genetic algorithms
    Wu Chuansheng
    Wu Cen
    Kang Lishan
    [J]. PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON INNOVATION & MANAGEMENT, 2005, : 432 - 437
  • [25] Applying genetic algorithms and support vector machines to the gene selection problem
    de Souza, Bruno Feres
    Carvalho, Andre C. P. L. F.
    Ticona, Waldo Cancino
    [J]. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2007, 18 (05) : 435 - 444
  • [26] Sparse Least Squares Support Vector Machines via Genetic Algorithms
    Silva, Juliana Peixoto
    da Rocha Neto, Ajalmar R.
    [J]. 2013 1ST BRICS COUNTRIES CONGRESS ON COMPUTATIONAL INTELLIGENCE AND 11TH BRAZILIAN CONGRESS ON COMPUTATIONAL INTELLIGENCE (BRICS-CCI & CBIC), 2013, : 248 - 253
  • [27] Imbalanced data classification via support vector machines and genetic algorithms
    Cervantes, Jair
    Li, Xiaoou
    Yu, Wen
    [J]. CONNECTION SCIENCE, 2014, 26 (04) : 335 - 348
  • [28] Support vector machines and genetic algorithms for soft-sensing modeling
    Sang Haifeng
    Yuan Weiqi
    Wang Fuli
    He Dakuo
    [J]. ADVANCES IN NEURAL NETWORKS - ISNN 2007, PT 3, PROCEEDINGS, 2007, 4493 : 330 - +
  • [29] Sparse Least Squares Support Vector Machines Based on Genetic Algorithms: A Feature Selection Approach
    Machado Araujo, Pedro Hericson
    Rocha Neto, Ajalmar R.
    [J]. ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2019, PT II, 2019, 11507 : 500 - 511
  • [30] Algorithms for Sparse Support Vector Machines
    Landeros, Alfonso
    Lange, Kenneth
    [J]. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2023, 32 (03) : 1097 - 1108
12345