The naïve Bayes classifier plays an important role among the classifiers based on supervised learning, although it requires strong condition on the feature independence assumptions. A measurement for the independency checking in the data preprocessing is necessary to guarantee the effectiveness of the classifier. Copula Theory is a mathematical tool in dependency modeling. In this paper, we recall elements of copulas and introduce a new algorithm to construct multiscale copula estimators which can be used for the independency testing to improve the accuracy of the Naïve Bayes classifier.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Supervise Learning With Copulas


    Contributors:
    Shen, Xiaoping (author) / Ewing, Robert L. (author) / Li, Jia (author)


    Publication date :

    2019-07-01


    Size :

    1286119 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SYSTEM AND METHOD TO SUPERVISE VEHICLE POSITIONING INTEGRITY

    GREEN ALON / TOBIN JAMES KEVIN / BATCHELOR ANDREW | European Patent Office | 2024

    Free access

    SYSTEM AND METHOD TO SUPERVISE VEHICLE POSITIONING INTEGRITY

    GREEN ALON / TOBIN JAMES KEVIN / BATCHELOR ANDREW | European Patent Office | 2024

    Free access

    SYSTEM AND METHOD TO SUPERVISE VEHICLE POSITIONING INTEGRITY

    GREEN ALON / TOBIN JAMES KEVIN / BATCHELOR ANDREW | European Patent Office | 2021

    Free access

    SYSTEM AND METHOD TO SUPERVISE VEHICLE POSITIONING INTEGRITY

    GREEN ALON / TOBIN JAMES KEVIN / BATCHELOR ANDREW | European Patent Office | 2021

    Free access

    Procédé statistique non supervisé de détection multivariée de courbes atypiques

    BERGERET FRANÇOIS / ALVES AMAURY / ARCHIMBAUD AURORE et al. | European Patent Office | 2023

    Free access