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http://cseweb.ucsd.edu/~elkan/254spring01/jdrishrep.pdf
Obtaining Calibrated Probability Estimates from Support Vector Machines Joseph Drish Department of Computer Science and Engineering 0114 University of California, San Diego La Jolla, California 92037-0114 [email protected] Abstract We use a technique known as binning to convert the outputs of support
https://www.researchgate.net/publication/2366041_Obtaining_Calibrated_Probability_Estimates_from_Support_Vector_Machines
Download Citation Obtaining Calibrated Probability Estimates from Support Vector Machines We use a technique known as binning to convert the outputs of support vector …
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.330.6954
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): In many supervised learning tasks a learned classifier automatically induces a ranking of test examples, making it possible to determine the relative likelihood that a given test example belongs to a certain class. However, for many applications this ranking is not sufficient, particularly when …
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.960
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We use a technique known as binning to convert the outputs of support vector machine (SVM) classifiers into well-calibrated probabilities. Using the KDD'98 data set as a testbed, we evaluate predicted probabilities using four metrics, and compare our results to those obtained by Zadrozny and …
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.161.3828
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We use a technique known as binning to convert the outputs of support vector machine (SVM) classifiers into well-calibrated probabilities. Using the KDD’98 data set as a testbed, we evaluate predicted probabilities using four metrics, and compare our results to those obtained by Zadrozny and …
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4410090/
Obtaining Well Calibrated Probabilities Using Bayesian Binning. ... To conduct the experiments on simulated datasets, we used two extreme classifiers: support vector machines (SVM) with linear and quadratic kernels. The choice of SVM with a linear kernel allows us to see how the calibration methods perform when the classification model makes ...Cited by: 91
https://scikit-learn.org/stable/modules/calibration.html
Probability calibration should be done on new data not used for model fitting. ... Obtaining calibrated probability estimates from decision trees and naive Bayesian classifiers, B. Zadrozny & C. Elkan, ICML 2001 ... Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods, J. Platt, (1999) 4 (1,2)
https://core.ac.uk/display/22951732
Obtaining Calibrated Probability Estimates from Support Vector Machines . ... it is necessary to convert the outputs of the classifier into well-calibrated posterior probabilities. A recent paper that addresses this problem is [7], which introduces new methods for estimating the probabilities from naive Bayes and decision tree classifiers ...Author: Joseph Drish
http://core.ac.uk/display/22499972
Obtaining Calibrated Probability Estimates from Support Vector Machines . By Joseph Drish. Abstract. We use a technique known as binning to convert the outputs of support vector machine (SVM) classifiers into well-calibrated probabilities. Using the KDD'98 data set as a testbed, we evaluate predicted probabilities using four metrics, and ...Author: Joseph Drish
https://link.springer.com/article/10.1007%2Fs10115-013-0670-6
Jul 12, 2013 · We propose a simple, effective method to mitigate the bias of probability estimates for imbalanced data that bags estimators independently calibrated over balanced bootstrap samples. This approach drastically improves performance on the minority instances without greatly affecting overall calibration.Cited by: 16
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