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Abstract: Probabilistic calibration is the task of producing reliable estimates of the conditional class probability P (class|observation) from the outputs of numerical classifiers. A recent comparative study [1] revealed that Isotonic Regression [2] and Platt Calibration [3] are most e#ective probabilistic calibration technique for a wide range of classifiers. (Update)
Active bibliography (related documents): More All
0.6: Obtaining Calibrated Probabilities from Boosting - Niculescu-Mizil, Caruana (2005) (Correct)
0.2: Learning with Local Models - Stefan Ruping University (2005) (Correct)
0.2: A Simple Method For Estimating Conditional Probabilities For.. - Stefan Uping Cs (Correct)
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BibTeX entry: (Update)
@misc{ fraunhofer-robust,
author = "Stefan Ruping Fraunhofer",
title = "Robust Probabilistic Calibration",
url = "citeseer.ist.psu.edu/760366.html" }
Citations (may not include all citations):667 UCI repository of machine learning databases (context) - Murphy, Aha - 1994
510 Robust Statistics (context) - Huber - 1981
377 Robust Regression and Outlier Detection (context) - Rousseeuw, Leroy - 1987
132 Least median of squares regression (context) - Rousseeuw - 1984
77 Probabilistic outputs for support vector machines and compar.. - Platt - 1999
6 Transforming classifier scores into accurate multiclass prob.. - Zadrozny, Elkan - 2002
5 Classification Rules in Standardized Partition Spaces (context) - Garczarek - 2002
2 Predicting good probabilities with supervised learning (context) - Niculescu-Mizil, Caruana - 2005
1 A simple method for estimating conditional probabilities in .. (context) - Ruping - 2004
1 Statistical comparisons of classifiers over multiple data se.. (context) - Demsar - 2006
Documents on the same site (http://www.stefan-rueping.de/index.html): More
Learning Interpretable Models - Dissertation Zur Erlangung (2006) (Correct)
Learning with Local Models - Stefan Ruping University (2005) (Correct)
Support Vector Machines in Relational Databases - Stefan Uping Cs (2002) (Correct)
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