On the public citation graph
A Critical Reassessment of the Saerens-Latinne-Decaestecker Algorithm for Posterior Probability Adjustment
0 signed · 57 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-0-387-30164-8openalex-snapshot
Encyclopedia of Machine Learning
inferredcites →https://doi.org/10.1007/978-1-4757-3264-1openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/s10044-016-0578-3openalex-snapshot
Obtaining calibrated probability using ROC Binning
inferredcites →https://doi.org/10.1007/s10618-008-0097-yopenalex-snapshot
Quantifying counts and costs via classification
inferredcites →https://doi.org/10.1007/s10618-013-0308-zopenalex-snapshot
Aggregative quantification for regression
inferredcites →https://doi.org/10.1007/s10791-019-09363-yopenalex-snapshot
Evaluation measures for quantification: an axiomatic approach
inferredcites →https://doi.org/10.1007/s10994-005-5256-4openalex-snapshot
A Response to Webb and Ting?s On the Application of ROC Analysis to Predict Classification Performance Under Varying Class Distributions
inferredcites →https://doi.org/10.1007/s13278-016-0327-zopenalex-snapshot
From classification to quantification in tweet sentiment analysis
inferredcites →https://doi.org/10.1016/j.ejor.2011.05.027openalex-snapshot
A probability-mapping algorithm for calibrating the posterior probabilities: A direct marketing application
inferredcites →https://doi.org/10.1016/j.inffus.2018.01.001openalex-snapshot
Dynamic ensemble selection for quantification tasks
inferredcites →https://doi.org/10.1016/j.knosys.2017.07.034openalex-snapshot
Approaches for credit scorecard calibration: An empirical analysis
inferredcites →https://doi.org/10.1016/j.patcog.2011.06.019openalex-snapshot
A unifying view on dataset shift in classification
inferredcites →https://doi.org/10.1016/j.patrec.2005.10.010openalex-snapshot
An introduction to ROC analysis
inferredcites →https://doi.org/10.1109/mis.2020.2979203openalex-snapshot
Cross-Lingual Sentiment Quantification
inferredcites →https://doi.org/10.1111/j.2517-6161.1977.tb01600.xopenalex-snapshot
Maximum Likelihood from Incomplete Data Via the EM Algorithm
inferredcites →https://doi.org/10.1145/1102351.1102430openalex-snapshot
Predicting good probabilities with supervised learning
inferredcites →https://doi.org/10.1145/1390156openalex-snapshot
Proceedings of the 25th international conference on Machine learning - ICML '08
inferredcites →https://doi.org/10.1145/3268928openalex-snapshot
Jointly Minimizing the Expected Costs of Review for Responsiveness and Privilege in E-Discovery
inferredcites →https://doi.org/10.1145/3269206.3269287openalex-snapshot
A Recurrent Neural Network for Sentiment Quantification
inferredcites →https://doi.org/10.1145/3341161.3342948openalex-snapshot
Quantification under class-conditional dataset shift
inferredcites →https://doi.org/10.1145/3385656openalex-snapshot
Better Classifier Calibration for Small Datasets
inferredcites →https://doi.org/10.1145/775047.775151openalex-snapshot
Transforming classifier scores into accurate multiclass probability estimates
inferredcites →https://doi.org/10.1162/089976602753284446openalex-snapshot
Adjusting the Outputs of a Classifier to New a Priori Probabilities: A Simple Procedure
inferredcites →https://doi.org/10.1175/1520-0450(1973)012<0595:anvpot>2.0.co;2openalex-snapshot
A New Vector Partition of the Probability Score
inferredcites →https://doi.org/10.1175/1520-0493(1950)078<0001:vofeit>2.0.co;2openalex-snapshot
VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY
inferredcites →https://doi.org/10.1175/2007waf2006116.1openalex-snapshot
Two Extra Components in the Brier Score Decomposition
inferredcites →https://doi.org/10.1198/016214506000001437openalex-snapshot
Strictly Proper Scoring Rules, Prediction, and Estimation
inferredcites →https://doi.org/10.1371/journal.pone.0263449openalex-snapshot
Tweet sentiment quantification: An experimental re-evaluation
inferredcites →https://doi.org/10.1561/1500000006openalex-snapshot
Email Spam Filtering: A Systematic Review
inferredcites →https://doi.org/10.2307/2987588openalex-snapshot
The Comparison and Evaluation of Forecasters
inferredcites →https://doi.org/10.48550/arxiv.1807.03929openalex-snapshot
Quantification under prior probability shift: the ratio estimator and its extensions
inferredcites →https://doi.org/10.5555/1005332.1016791openalex-snapshot
Probability Estimates for Multi-class Classification by Pairwise Coupling
inferredcites →https://doi.org/10.5555/1484611.1484627openalex-snapshot
Computational methods in authorship attribution
inferredcites →https://doi.org/10.7551/mitpress/9780262170055.001.0001openalex-snapshot
Dataset Shift in Machine Learning
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-030-72240-1_6openalex-snapshot
Re-assessing the “Classify and Count” Quantification Method
inferred← citeshttps://doi.org/10.1007/978-3-031-13643-6_23openalex-snapshot
A Concise Overview of LeQua@CLEF 2022: Learning to Quantify
inferred← citeshttps://doi.org/10.1007/978-3-031-20467-8_1openalex-snapshot
The Case for Quantification
inferred← citeshttps://doi.org/10.1007/978-3-031-20467-8_2openalex-snapshot
Applications of Quantification
inferred← citeshttps://doi.org/10.1007/978-3-031-20467-8_3openalex-snapshot
Evaluation of Quantification Algorithms
inferred← citeshttps://doi.org/10.1007/978-3-031-20467-8_4openalex-snapshot
Methods for Learning to Quantify
inferred← citeshttps://doi.org/10.1007/978-3-031-20467-8_6openalex-snapshot
The Quantification Landscape
inferred← citeshttps://doi.org/10.1007/s10618-024-01014-1openalex-snapshot
Binary quantification and dataset shift: an experimental investigation
inferred← citeshttps://doi.org/10.1007/s10994-024-06726-5openalex-snapshot
Kernel density estimation for multiclass quantification
inferred← citeshttps://doi.org/10.1016/j.ipm.2026.105095openalex-snapshot
PAIRE: A testbed for measuring dataset diversity and model fairness under limited sensitive information
inferred← citeshttps://doi.org/10.1016/j.iswa.2023.200209openalex-snapshot
Improved risk minimization algorithms for technology-assisted review
inferred← citeshttps://doi.org/10.1093/llc/fqad019openalex-snapshot
Unravelling interlanguage facts via explainable machine learning
inferred← citeshttps://doi.org/10.1109/icdm51629.2021.00121openalex-snapshot
Gain-Some-Lose-Some: Reliable Quantification Under General Dataset Shift
inferred← citeshttps://doi.org/10.1109/taffc.2023.3301956openalex-snapshot
Confidence-Aware Sentiment Quantification via Sentiment Perturbation Modeling
inferred← citeshttps://doi.org/10.1109/tkde.2023.3349286openalex-snapshot
Dynamic Quantification With Constrained Error Under Unknown General Dataset Shift
inferred← citeshttps://doi.org/10.1145/3459637.3482015openalex-snapshot
QuaPy: A Python-Based Framework for Quantification
inferred← citeshttps://doi.org/10.3934/era.2023303openalex-snapshot
A rational resource allocation method for multimedia network teaching reform based on Bayesian partition data mining
inferred← citeshttps://doi.org/10.3934/fods.2026019openalex-snapshot
Factorizable joint shift revisited
inferred← citeshttps://doi.org/10.70675/8848fbe1ze611z43d3z937azf54fa35decf2openalex-snapshot
A unified framework for label shift quantification
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.