Review



fitcensemble function  (MathWorks Inc)


Bioz Verified Symbol MathWorks Inc is a verified supplier  
  • Logo
  • About
  • News
  • Press Release
  • Team
  • Advisors
  • Partners
  • Contact
  • Bioz Stars
  • Bioz vStars
    • 90
      Buy from Supplier

    Structured Review

    MathWorks Inc fitcensemble function
    Fitcensemble Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/fitcensemble function/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    fitcensemble function - by Bioz Stars, 2026-04
    90/100 stars

    Images



    Similar Products

    fitcensemble function  (MathWorks Inc)
    90
    MathWorks Inc fitcensemble function
    Fitcensemble Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/fitcensemble function/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    fitcensemble function - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier
    fitcensemble function with a subspace of knn  (MathWorks Inc)
    90
    MathWorks Inc fitcensemble function with a subspace of knn
    Fitcensemble Function With A Subspace Of Knn, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/fitcensemble function with a subspace of knn/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    fitcensemble function with a subspace of knn - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier
    matlab's 'fitcensemble' function  (MathWorks Inc)
    90
    MathWorks Inc matlab's 'fitcensemble' function
    Comparing non-hierarchical and hierarchical approaches for classifying calls based on source identity. Mean ± s.d. precisions and recalls with corresponding p -values for testing the hypotheses: mean precisions/recalls of <t> non-hierarchical = hierarchical. </t> Both approaches were tested using the Balanced-X datasets.
    Matlab's 'fitcensemble' Function, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/matlab's 'fitcensemble' function/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    matlab's 'fitcensemble' function - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier
    fitcensemble function gentleboost method  (MathWorks Inc)
    90
    MathWorks Inc fitcensemble function gentleboost method
    Comparing non-hierarchical and hierarchical approaches for classifying calls based on source identity. Mean ± s.d. precisions and recalls with corresponding p -values for testing the hypotheses: mean precisions/recalls of <t> non-hierarchical = hierarchical. </t> Both approaches were tested using the Balanced-X datasets.
    Fitcensemble Function Gentleboost Method, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/fitcensemble function gentleboost method/product/MathWorks Inc
    Average 90 stars, based on 1 article reviews
    fitcensemble function gentleboost method - by Bioz Stars, 2026-04
    90/100 stars
    		  Buy from Supplier

    Image Search Results


    Comparing non-hierarchical and hierarchical approaches for classifying calls based on source identity. Mean ± s.d. precisions and recalls with corresponding p -values for testing the hypotheses: mean precisions/recalls of non-hierarchical = hierarchical. Both approaches were tested using the Balanced-X datasets.

    Journal: Journal of the Royal Society Interface

    Article Title: Who is calling? Optimizing source identification from marmoset vocalizations with hierarchical machine learning classifiers

    doi: 10.1098/rsif.2023.0399

    Figure Lengend Snippet: Comparing non-hierarchical and hierarchical approaches for classifying calls based on source identity. Mean ± s.d. precisions and recalls with corresponding p -values for testing the hypotheses: mean precisions/recalls of non-hierarchical = hierarchical. Both approaches were tested using the Balanced-X datasets.

    Article Snippet: We first trained AdaBoost (using MATLAB's ‘fitcensemble’, ‘AdaboostM1′, and ‘AdaboostM2′ functions) on Imbalanced-X, Balanced-X, Balanced197-X, and Balanced99-X datasets to classify calls based on source identity—as a direct or ‘non-hierarchical approach’ (in contrast to the hierarchical approach that was used later)—to determine source identity from calls.

    Techniques: