Predicting good probabilities with supervised learning

Keywords

Margin (machine learning), Artificial intelligence, Calibration, Machine learning, Computer science

References

• Tree Induction for Probability-Based RankingFoster Provost, Pedro Domingos — 2003
• Making large scale SVM learning practicalThorsten Joachims — 1998
• Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiersBianca Zadrozny, Charles Elkan — 2001
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• Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood MethodsJohn Platt — 1999
• Transforming classifier scores into accurate multiclass probability estimatesBianca Zadrozny, Charles Elkan — 2002
• AN EMPIRICAL DISTRIBUTION FUNCTION FOR SAMPLING WITH INCOMPLETE INFORMATIONMiriam Ayer, H. D. Brunk, George M. Ewing — 1955
• UCI Repository of machine learning databasesCatherine Blake — 1998
• Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost DistributionsFoster Provost, Tom Fawcett — 1997
• Order restricted statistical inferenceThomas J. Santner, Tim Robertson, F. T. Wright — 1988
• Obtaining Calibrated Probabilities from BoostingAlexandru Niculescu-Mizil, Rich Caruana — 2005
• Support Vector Machine Classifiers as Applied to AVIRIS DataAnthony G. Gualtieri, Samir R. Chettri, R. Cromp — 1999

Cited by

• Aro: a machine learning approach to identifying single molecules and estimating classification error in fluorescence microscopy imagesNerina Dirindin — 2015
• The CCB-ID approach to tree species mapping with airborne imaging spectroscopyJan Mattsson, John Wadbro, Pontus Thorsson — 2018
• Uncertainty-aware performance assessment of optical imaging modalities with invertible neural networksRoberta Sebastiani, Daniele Dalli, Francesca Montagnini — 2019
• Machine learning in health informatics: making better use of domain expertsCarla E. Brodley, Byron Wallace — 2012
• Prediction of metabolomic and transcriptomic patient profilesInka J. Appel — 2012
• Towards personalized medicine using systems biology and machine learningS. Drăghici, Calin Voichita — 2013
• Multilabel Prediction via CalibrationNikos Karampatziakis, Paul Mineiro — 2015
• I2VM: Incremental import vector machinesRibana Roscher, Wolfgang Förstner, Björn Waske — 2012
• Winning the KDD Cup Orange Challenge with Ensemble SelectionAlexandru Niculescu-Mizil, Claudia Perlich, Grzegorz Świrszcz — 2009
• On Calibrated Predictions for Auction Selection MechanismsH. Brendan McMahan, Omkar Muralidharan — 2012
• Computational Linguistic Models of Deceptive Opinion SpamMyle Ott — 2013
• Evidential calibration of binary SVM classifiersPhilippe Xu, Franck Davoine, Hongbin Zha — 2016
• Efficient techniques for cost-sensitive learning with multiple cost considerationsTaizhi Wang — 2013
• Advances in SCA and RF-DNA Fingerprinting Through Enhanced Linear Regression Attacks and Application of Random Forest ClassifiersHiren J. Patel — 2014
• Information fusion for scene understanding. (Fusion d'informations pour la compréhesion de scènes)Philippe Xu — 2014
• Interactive Learning Protocols for Natural Language ApplicationsKevin Small — 2009
• On the Uniform Convergence of Consistent Confidence MeasuresYihan Gao, Aditya Parameswaran — 2015
• Supervised geochemical anomaly detection by pattern recognitionArman Mohammadi Gonbadi, Seyed Hasan Tabatabaei, Emmanuel John M. Carranza — 2015
• Aide au diagnostic du cancer de la prostate par IRM multi-paramétrique : une approche par classification superviséeÉmilie Niaf — 2012
• Computer Vision – ECCV 2014David J. Fleet, Tinne Tuytelaars, Bernt Schiele — 2014

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