SEVERAL SPECIAL FUNCTIONS IN FRACTALS AND APPLICATIONS OF THE FRACTAL IN MACHINE LEARNING

被引:0
|
作者
Wang, Jun [1 ,2 ]
Cao, Lei [1 ,2 ]
Chen, Xiliang [1 ,2 ]
Tang, Wei [1 ,2 ]
Xu, Zhixiong [3 ]
机构
[1] Army Engn Univ PLA, Nanjing 211101, Peoples R China
[2] Troops 78092, Chengdu 610031, Peoples R China
[3] Army Acad Border & Coastal Def, Xian 710100, Peoples R China
基金
中国国家自然科学基金;
关键词
Unbounded Variation; Continuous Function; Machine Learning; Fractals; FRACTIONAL CALCULUS; DIMENSIONS;
D O I
10.1142/S0218348X22500311
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
The focus of this paper is to study unbounded variation functions from the perspective of Holder conditions. Three special unbounded variation functions have been constructed. The first is a continuous function of unbounded variation that satisfies the Holder condition of a given order and the second is a continuous function of unbounded variation that does not satisfy the Holder condition of any order. The third is a continuous function of unbounded variation defined on any sub-interval of the interval I. Then, specific fractal dimension analysis of the above functions and relevant conclusions have been investigated. Finally, combining functional analysis and reinforcement learning, the convergence of reinforcement learning algorithms can be proved in unified framework.
引用
收藏
页数:9
相关论文
共 50 条
  • [1] Introduction to this special section: Machine learning applications
    Shaw S.
    Sharma A.
    Baraniuk R.
    Roy B.
    Leading Edge, 2019, 38 (07) : 510
  • [2] A REVIEW OF FRACTAL FUNCTIONS AND APPLICATIONS
    Wang, Xuefei
    Zhao, Chunxia
    Yuan, Xia
    FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY, 2022, 30 (06)
  • [3] Special issue: Applications of machine learning to ecological modelling - Preface
    Recknagel, F
    ECOLOGICAL MODELLING, 2001, 146 (1-3) : 1 - 2
  • [4] Special issue on information processing and machine learning for applications of engineering
    Travieso, Carlos M.
    Fodor, Janos
    Alonso, Jesus B.
    NEUROCOMPUTING, 2015, 150 : 347 - 348
  • [5] Linearly constrained reconstruction of functions by kernels with applications to machine learning
    Schaback, R
    Werner, J
    ADVANCES IN COMPUTATIONAL MATHEMATICS, 2006, 25 (1-3) : 237 - 258
  • [6] Machine Learning Computers With Fractal von Neumann Architecture
    Zhao, Yongwei
    Fan, Zhe
    Du, Zidong
    Zhi, Tian
    Li, Ling
    Guo, Qi
    Liu, Shaoli
    Xu, Zhiwei
    Chen, Tianshi
    Chen, Yunji
    IEEE TRANSACTIONS ON COMPUTERS, 2020, 69 (07) : 998 - 1014
  • [7] Linearly constrained reconstruction of functions by kernels with applications to machine learning
    R. Schaback
    J. Werner
    Advances in Computational Mathematics, 2006, 25 : 237 - 258
  • [8] Special issue on big data computing service and machine learning applications
    Potika, Katerina
    Eirinaki, Magdalini
    Vitali, Monica
    Bernasconi, Anna
    Fujioka, Hiroyuki
    FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2025, 171
  • [9] Machine Learning-Based Scoring Functions, Development and Applications with SAnDReS
    Bitencourt-Ferreira, Gabriela
    Rizzotto, Camila
    de Azevedo Junior, Walter Filgueira
    CURRENT MEDICINAL CHEMISTRY, 2021, 28 (09) : 1746 - 1756
  • [10] Special Issue Review: Artificial Intelligence and Machine Learning Applications in Remote Sensing
    Chen, Ying-Nong
    Fan, Kuo-Chin
    Chang, Yang-Lang
    Moriyama, Toshifumi
    REMOTE SENSING, 2023, 15 (03)