Analyses on Influence of Training Data Set to Neural Network Supervised Learning Performance

被引:0
|
作者
Zhou, Yu [1 ]
Wu, Yali [1 ]
机构
[1] N China Inst Water Conservancy & Hydroelect Power, Zhengzhou 450011, Peoples R China
关键词
Neural network; supervised learning performance; training data set;
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
The influence of training data set on the supervised learning performance of artificial neural network (ANN) is studied in detail in this paper. First, some illustrative experiments are conducted, which verify that different training data set can lead to different supervised learning performance of ANN; secondly. the necessity of carrying data preprocessing to training data set is analyzed. and how training data set affect the supervised learning is summarized: at last, the existing methods about improving performance of ANN by using high-quality training data are discussed.
引用
收藏
页码:19 / 25
页数:7
相关论文
共 50 条
  • [41] Deep neural network for water/fat separation: Supervised training, unsupervised training, and no training
    Jafari, Ramin
    Spincemaille, Pascal
    Zhang, Jinwei
    Nguyen, Thanh D.
    Luo, Xianfu
    Cho, Junghun
    Margolis, Daniel
    Prince, Martin R.
    Wang, Yi
    MAGNETIC RESONANCE IN MEDICINE, 2021, 85 (04) : 2263 - 2277
  • [42] Improving supervised learning performance by using fuzzy clustering method to select training data
    Guan, Donghai
    Yuan, Weiwei
    Lee, Young-Koo
    Gavrilov, Andrey
    Lee, Sungyoung
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2008, 19 (4-5) : 321 - 334
  • [43] RANDOM NEURAL NETWORK MODEL FOR SUPERVISED LEARNING PROBLEMS
    Basterrech, S.
    Rubino, G.
    NEURAL NETWORK WORLD, 2015, 25 (05) : 457 - 499
  • [44] Supervised learning in neural networks without feedback network
    Brandt, RD
    Lin, F
    PROCEEDINGS OF THE 1996 IEEE INTERNATIONAL SYMPOSIUM ON INTELLIGENT CONTROL, 1996, : 86 - 90
  • [45] Supervised Learning for Convolutional Neural Network with Barlow Twins
    Murugan, Ramyaa
    Mojoo, Jonathan
    Kurita, Takio
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2022, PT IV, 2022, 13532 : 484 - 495
  • [46] A dynamic growing neural network for supervised or unsupervised learning
    Tian, Daxin
    Liu, Yanheng
    Wei, Da
    WCICA 2006: SIXTH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION, VOLS 1-12, CONFERENCE PROCEEDINGS, 2006, : 2886 - 2890
  • [47] Modeling of Supervised ADALINE Neural Network Learning Technique
    Pellakuri, Vidyullatha
    Rao, D. Rajeswara
    Murhty, J. V. R.
    PROCEEDINGS OF THE 2016 2ND INTERNATIONAL CONFERENCE ON CONTEMPORARY COMPUTING AND INFORMATICS (IC3I), 2016, : 17 - 22
  • [48] A Neural Network for Semi-supervised Learning on Manifolds
    Genkin, Alexander
    Sengupta, Anirvan M.
    Chklovskii, Dmitri
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2019: THEORETICAL NEURAL COMPUTATION, PT I, 2019, 11727 : 375 - 386
  • [49] Supervised Learning in a Multilayer, Nonlinear Chemical Neural Network
    Arredondo, David
    Lakin, Matthew R.
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (10) : 7734 - 7745
  • [50] Supervised incremental learning with the fuzzy ARTMAP neural network
    Connolly, Jean-Francois
    Granger, Eric
    Sabourin, Robert
    ARTIFICIAL NEURAL NETWORKS IN PATTERN RECOGNITION, PROCEEDINGS, 2008, 5064 : 66 - 77