BETA DIVERGENCE AND KERNELS METHODS WITH SUPPORT VECTOR MACHINE (SVM)
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Publication:6170167
DOI10.17654/0972086323006OpenAlexW4375862932MaRDI QIDQ6170167
Papa Ngom, MacOumba Ndour, MacTar Ndaw
Publication date: 15 August 2023
Published in: Far East Journal of Theoretical Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.17654/0972086323006
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric estimation (62G05) Image analysis in multivariate analysis (62H35)
Cites Work
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- Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses
- Spirals in Hilbert space: with an application in information theory
- Families of alpha-, beta- and gamma-divergences: flexible and robust measures of similarities
- Metrics defined by Bregman divergences. II
- Cutoff time based on generalized divergence measure
- Discriminating between the log-normal and generalized exponential distributions
- Support-vector networks
- Uniform in bandwidth consistency of kernel-type function estimators
- Discriminating between the Weibull and log-normal distributions for Type-II censored data
- Discriminating between the Weibull and log-normal distributions
- Robust and efficient estimation by minimising a density power divergence
- Generalised information criteria in model selection
- Fast Projection‐Based Methods for the Least Squares Nonnegative Matrix Approximation Problem
- TYPE II TOPP-LEONE INVERSE WEIBULL DISTRIBUTION WITH STATISTICAL PROPERTIES AND APPLICATIONS
- Discriminating Between the Log-Normal and Log-Logistic Distributions
- On Bregman Distances and Divergences of Probability Measures
- Learning Theory and Kernel Machines
- On Information and Sufficiency
- Discrimination between Gamma and Log-Normal Distributions by Ratio of Minimized Kullback-Leibler Divergence
- An empirical process approach to the uniform consistency of kernel-type function estimators
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