Probability Estimates for Multi-class Classification by Pairwise Coupling
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Summary
Two approaches for obtaining class probabilities can be reduced to linear systems and are easy to implement and shown conceptually and experimentally that the proposed approaches are more stable than the two existing popular methods.
- Type
- article
- Published
- 2003-12-09
- Cited by
- 2,025
- References
- 29
- OpenAlex
- https://openalex.org/W2103568877
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7664224
Keywords
Pairwise comparison, Voting, Class (philosophy), Computer science, Coupling (piping)
References
- Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods
- Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
- Classification by Pairwise Coupling
- Statistical behavior and consistency of classification methods based on convex risk minimization
- VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY
- UCI Repository of machine learning databases
- A training algorithm for optimal margin classifiers
- A Database for Handwritten Text Recognition Research
- Reducing multiclass to binary: a unifying approach for margin classifiers
- Die Berechnung der Turnier-Ergebnisse als ein Maximumproblem der Wahrscheinlichkeitsrechnung
- Gradient-based learning applied to document recognition
- Support-Vector Networks
- Machine Learning, Neural and Statistical Classification
- MM algorithms for generalized Bradley-Terry models
- Pairwise Neural Network Classifiers with Probabilistic Outputs
- Stochastic Processes
- Random Forests
- UCI Repository of Machine Learning Databases
- Probability Estimates for Multi-class Classification by Pairwise Coupling
- Another approach to polychotomous classification
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