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Learning from partially supervised data using mixture models and belief functions

https://doi.org/10.1016/j.patcog.2008.07.014
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Finite Mixture Models
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On the unicity of dempster rule of combination
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Partially Supervised Learning by a Credal EM Approach
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Learning from Ambiguously Labeled Examples
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A k-Nearest Neighbor Classification Rule Based on Dempster-Shafer Theory
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Upper and Lower Probabilities Induced by a Multivalued Mapping
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Belief Classification Approach Based on Generalized Credal EM
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A Mathematical Theory of Arguments for Statistical Evidence
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EM Algorithm for Partially Known Labels
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Uncertainty-Based Information
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Numerical Representation of Uncertainty
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Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering
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Semi-supervised learning with an imperfect supervisor
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The transferable belief model
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Gaussian parsimonious clustering models
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Uniqueness of information measure in the theory of evidence
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Belief functions: The disjunctive rule of combination and the generalized Bayesian theorem
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High-dimensional data clustering
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Belief functions on real numbers
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On the plausibility transformation method for translating belief function models to probability models
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Least committed basic belief density induced by a multivariate Gaussian: Formulation with applications
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Pruning belief decision tree methods in averaging and conjunctive approaches
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Decision trees as possibilistic classifiers
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Classification in the presence of class noise using a probabilistic Kernel Fisher method
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Handling possibilistic labels in pattern classification using evidential reasoning
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Belief decision trees: theoretical foundations
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Belief function independence: I. The marginal case
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Belief function independence: II. The conditional case
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Text Classification from Labeled and Unlabeled Documents using EM
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Estimating the Linear Discriminant Function from Initial Samples Containing a Small Number of Unclassified Observations
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Normal Discrimination with Unclassified Observations
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High-Dimensional Discriminant Analysis
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A k-nearest neighbor classification rule based on Dempster-Shafer theory
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A boosting approach to remove class label noise
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Uncertainty in Artificial Intelligence
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Target Identification Based on the Transferable Belief Model Interpretation of Dempster–Shafer Model
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Classification Using Belief Functions: Relationship Between Case-Based and Model-Based Approaches
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Upper Probabilities Based Only on the Likelihood Function
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Maximum Likelihood from Incomplete Data Via the EM Algorithm
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A MATHEMATICAL THEORY OF EVIDENCE
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Elicitation of Expert Opinions for Constructing Belief Functions
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Proceedings of the 24th international conference on Machine learning
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Upper and Lower Probabilities Induced by a Multivalued Mapping
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The EM Algorithm and Extensions
inferredcites →https://doi.org/10.2307/2529141openalex-snapshot
A Comparison of Iterative Maximum Likelihood Estimates of the Parameters of a Mixture of Two Normal Distributions Under Three Different Types of Sample
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Model-Based Gaussian and Non-Gaussian Clustering
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A Mathematical Theory of Evidence
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Semi-Supervised Learning

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Information fusion and decision-making using belief functions : application to therapeutic monitoring of cancer
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