Training algorithms for fuzzy support vector machines with noisy data

被引：15
|
作者：
Lin, CF [1 ]
Wang, SD [1 ]
机构：
[1] Natl Taiwan Univ, Dept Elect Engn, Taipei 106, Taiwan
来源：
2003 IEEE XIII WORKSHOP ON NEURAL NETWORKS FOR SIGNAL PROCESSING - NNSP'03 | 2003年
关键词：
D O I：
10.1109/NNSP.2003.1318051
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Fuzzy support vector machines (FSVMs) provide a method to classify data with noises or outliers. Each data point is associated with a fuzzy membership that can reflect their relative degrees as meaningful data. In this paper, we investigate and compare two strategies of automatically setting the fuzzy memberships of data points. It makes the usage of FSVMs easier in the application of reducing the effects of noises or outliers. The experiments show that the generalization error of FSVMs is comparable to other methods on benchmark datasets.
引用
收藏
页码：517 / 526
页数：10
相关论文
共 50 条
  • [1] Training algorithms for fuzzy support vector machines with noisy data
    Lin, CF
    Wang, SD
    PATTERN RECOGNITION LETTERS, 2004, 25 (14) : 1647 - 1656
  • [2] Relaxed constraints support vector machines for noisy data
    Sabzekar, Mostafa
    Yazdi, Hadi Sadoghi
    Naghibzadeh, Mahmoud
    NEURAL COMPUTING & APPLICATIONS, 2011, 20 (05): : 671 - 685
  • [3] Relaxed constraints support vector machines for noisy data
    Mostafa Sabzekar
    Hadi Sadoghi Yazdi
    Mahmoud Naghibzadeh
    Neural Computing and Applications, 2011, 20 : 671 - 685
  • [4] Provably Fast Training Algorithms for Support Vector Machines
    José L. Balcázar
    Yang Dai
    Junichi Tanaka
    Osamu Watanabe
    Theory of Computing Systems, 2008, 42 : 568 - 595
  • [5] Provably fast training algorithms for support vector machines
    Balcazar, Jose L.
    Dai, Yang
    Tanaka, Junichi
    Watanabe, Osamu
    THEORY OF COMPUTING SYSTEMS, 2008, 42 (04) : 568 - 595
  • [6] Provably fast training algorithms for Support Vector Machines
    Balcázar, JL
    Dai, Y
    Watanabe, O
    2001 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, 2001, : 43 - 50
  • [7] Training data selection for support vector machines
    Wang, JG
    Neskovic, P
    Cooper, LN
    ADVANCES IN NATURAL COMPUTATION, PT 1, PROCEEDINGS, 2005, 3610 : 554 - 564
  • [8] Feasible Direction Decomposition Algorithms for Training Support Vector Machines
    Pavel Laskov
    Machine Learning, 2002, 46 : 315 - 349
  • [9] Feasible direction decomposition algorithms for training support vector machines
    Laskov, P
    MACHINE LEARNING, 2002, 46 (1-3) : 315 - 349
  • [10] Fuzzy Multiclass Support Vector Machines for Unbalanced Data
    Wu, Yuanyuan
    Shen, Liyong
    Zhang, Sanguo
    2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2017, : 2227 - 2231
12345