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44 ta ish

Ish: Assessment of a probabilistic supervised machine learning method to estimate biomass expansion and conversion factors: a case study on cedar and pine trees

  1. Scikit-learn: Machine Learning in Python

    Fabián Pedregosa, Gaël Varoquaux, Alexandre Gramfort +16

    Preprint201214 iqtibos
    ABI

  2. An Introduction to the Bootstrap

    Bradley Efron, Robert Tibshirani

    Kitob19935 iqtibos
    ABI

  3. Nonlinear Regression Analysis and Its Applications

    Douglas M. Bates, Donald G. Watts

    Kitob19884 iqtibos
    ABI

  4. Regeneration of Lebanon cedar (Cedrus libani A. Rich.) on karstic lands in Turkey

    Melih Boydak

    Maqola20023 iqtibos
    ABI

  5. The Kolmogorov-Smirnov Test for Goodness of Fit

    Frank J. Massey

    Maqola19513 iqtibos
    ABI

  6. Bootstrap Methods: Another Look at the Jackknife

    Bradley Efron

    Bob19922 iqtibos
    ABI

  7. Biomass expansion factors for Sitka spruce (Picea sitchensis (Bong.) Carr.) in Ireland

    Brian Tobin, Maarten Nieuwenhuis

    Maqola20052 iqtibos
    ABI

  8. On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown

    Hubert W. Lilliefors

    Maqola19672 iqtibos
    ABI

  9. Biomass Expansion Factors of Natural Japanese Red Pine (Pinus densiflora) Forests in Korea

    Xiaodong Li, Myong Jong Yi, Yowhan Son +5

    Maqola20102 iqtibos
    ABI

  10. Generalized functions of biomass expansion factors for conifers and broadleaved by stand age, growing stock and site index

    Maurizio Teobaldelli, Zoltán Somogyi, Mirco Migliavacca +1

    Maqola20082 iqtibos
    ABI

  11. Biomass expansion factors (BEFs) for Scots pine, Norway spruce and birch according to stand age for boreal forests

    Aleksi Lehtonen, Raisa Mäkipää, Juha Heikkinen +2

    Maqola20042 iqtibos
    ABI

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