On the Theoretical Convergence and Error Sensitivity Analysis of Yayambo for Fusion of Probabilistic Classifier Outputs.
Jordan F. MasakunaPierre K. KafundaMardochee L. KayembePublished in: FUSION (2022)
Keyphrases
- sensitivity analysis
- managerial insights
- classifier combination
- fusion methods
- influence diagrams
- fusion process
- training data
- probabilistic model
- fusion strategies
- minimum error
- variational inequalities
- information fusion
- generative model
- support vector
- false negative
- theoretical analysis
- fusion method
- linear classifiers
- error rate
- decision trees
- bias variance decomposition
- convergence rate
- lower error rates
- generalization error
- data fusion
- support vector machine
- bayesian networks
- feature selection
- classifier fusion
- learning algorithm
- classification algorithm
- training set
- multiple features
- svm classifier
- multiple classifier systems
- class labels
- feature set