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An introduction to Majorization‐Minimization algorithms for machine learning and statistical estimation
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Statistical Learning Theory
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A New Approach to Estimating Switching Regressions
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Optimization Transfer Using Surrogate Objective Functions
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Regression Models for Multivariate Count Data
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On the Existence of Maximum Likelihood Estimates in Logistic Regression Models
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Convergent Computation by Iterative Majorization: Theory and Applications in Multidimensional Data Analysis
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Incremental Majorization-Minimization Optimization with Application to Large-Scale Machine Learning
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Next-Generation Statistical Genetics: Modeling, Penalization, and Optimization in High-Dimensional Data
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Nonconvex Optimization via MM Algorithms: Convergence Theory
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An introduction to MM algorithms for machine learning and statistical estimation
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Practical and theoretical aspects of mixture‐of‐experts modeling: An overview
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A novel minorization–maximization framework for simultaneous feature selection and clustering of high-dimensional count data
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The Inverse Problem of Positive Autoconvolution
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BoltzMM: an R package for maximum pseudolikelihood estimation of fully-visible Boltzmann machines
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A New Composite Dissimilarity Measure for Planar Curves Based on Higher-Order Derivatives
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Μοdel selectiοn and apprοximatiοn in high-dimensiοnal mixtures οf experts mοdels : from theory to practice
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