Kernel Function Selection

Kernel Function Selection is the process of choosing a specific kernel function within the class of non-parametric density estimation methods that best balances bias and variance in the estimated probability density function according to the data structure and underlying assumptions.

184

Publications

8K

Citations

567

Authors

267

Institutions

Publications per year

2017–2026

67

Authors

567

  1. NM
    Nancy McDonald

    National Time Service Center, China

    3 publications · 81 citations

    3

    h-index

  2. JW
    Jingbin Wang

    Vanderbilt University Medical Center, United States

    3 publications · 81 citations

    3

    h-index

  3. HW
    Haoxiang Wang

    Chinese University of Hong Kong, Hong Kong SAR China

    2 publications · 61 citations

    2

    h-index

  4. SC
    Steven C. H. Hoi

    Nanyang Technological University, Singapore

    2 publications · 93 citations

    2

    h-index

  5. BK
    Bor‐Chen Kuo

    National Taichung University of Education, Taiwan

    2 publications · 70 citations

    2

    h-index

1–5 of 567

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 95 citations

    5

    h-index

  2. IEEE Access

    3 publications · 149 citations

    3

    h-index

  3. Advanced materials research

    AMR · 2 publications · 26 citations

    2

    h-index

  4. Journal of Intelligent & Fuzzy Systems

    2 publications · 35 citations

    2

    h-index

  5. ISA Transactions

    2 publications · 73 citations

    2

    h-index

1–5