The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A Probabilistic Theory of Pattern Recognition
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“To train the PFC classification unit in the model, we used a random splits procedure, which has been shown to provide a good estimate of the expected error of a classifier (56).”
From A feedforward architecture accounts for rapid categorization (2007) · cites this paper as [56]
In the citing paper
“It is clearly not, in general, a consistent estimate (Devroye et al., 1996, Chapter 5).”
From A random forest guided tour (2016) · cites this paper as Devroye et al. (1996)
Citations
Cites
An introduction to probability theory and its applications
(1958) · Journal of the Franklin Institute · doi:10.1016/0016-0032(58)90688-4 · Cited by 29,621
Loève, M. (1963) · Andalas University Repository (Andalas University) · Cited by 5,938
Probability: Theory and Examples.
Prewitt, K., & Durrett, R. T. (1992) · Journal of the American Statistical Association · doi:10.2307/2290301 · Cited by 3,238
Probability and Random Processes
Grimmett, G., & Stirzaker, D. (2004) · Elsevier eBooks · doi:10.1016/b978-0-12-172651-5.x5000-3 · Cited by 2,607
Probability Theory: Independence, Interchangeability, Martingales
Lund, R., Chow, Y. S., & Teicher, H. (1998) · Journal of the American Statistical Association · doi:10.2307/2670078 · Cited by 1,095
Real Analysis and Probability.
Ross, K. A., & ASH, R. B. (1975) · American Mathematical Monthly · doi:10.2307/2319153 · Cited by 727
Cited by
Sorted by citations
Items 501–600 of 1,002
Unraveling gene-gene interactions regulated by ligands of the aryl hydrocarbon receptor
Johnson, C. D., Balagurunathan, Y., Tadesse, M. G., Falahatpisheh, M. H., Brun, M., Walker, M. K., Dougherty, E. R., & Ramos, K. S. (2004) · Environmental Health Perspectives · doi:10.1289/ehp.6758 · Cited by 55
Square root penalty: Adaptation to the margin in classification and in edge estimation
Tsybakov, A. B., & van de Geer, S. (2005) · The Annals of Statistics · doi:10.1214/009053604000001066 · Cited by 57
Deterministic Design for Neural Network Learning: An Approach Based on Discrepancy
Cervellera, C., & Marco Muselli, B. (2004) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.824413 · Cited by 55
Pattern Recognition, Statistical
Hofmann, T. (2005) · Encyclopedia of Cognitive Science · doi:10.1002/0470018860.s00013 · Cited by 57
Towards convergence rate analysis of random forests for classification
Gao, W. G., Xu, F., & Zhou, Z.-H. (2022) · Artificial Intelligence · doi:10.1016/j.artint.2022.103788 · Cited by 62
A Population Background for Nonparametric Density-Based Clustering
Chacón, J. E. (2015) · Statistical Science · doi:10.1214/15-sts526 · Cited by 56
SURE Guided Gaussian Mixture Image Denoising
Wang, Y., & Morel, J. (2013) · SIAM Journal on Imaging Sciences · doi:10.1137/120901131 · Cited by 56
Universal Estimation of Information Measures for Analog Sources
Wang, Q., Kulkarni, S. R., & Verdú, S. (2009) · Foundations and Trends® in Communications and Information Theory · doi:10.1561/0100000021 · Cited by 52
Exact performance of error estimators for discrete classifiers
Braga-Neto, U., & Dougherty, E. R. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2005.02.013 · Cited by 56
Bubeck, S., & Ulrike Von Luxburg, A. (2009) · doi:10.5555/1577069.1577092 · Cited by 56
A Fast Hyperspectral Feature Selection Method Based on Band Correlation Analysis
Zhang, W., Li, X., & Zhao, L. (2018) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2018.2853805 · Cited by 56
Impact of error estimation on feature selection
Chao Sima, A., Attoor, S. N., Brag-Neto, U., Lowey, J., Suh, E. B., & Dougherty, E. R. (2005) · Pattern Recognition · doi:10.1016/j.patcog.2005.03.026 · Cited by 56
An affine invariant k -nearest neighbor regression estimate
Gérard Biau, A., Devroye, L., Dujmović, V., & Krzyżak, A. (2012) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2012.05.020 · Cited by 55
Self-citationA multiresolution time-dependent entropy method for QRS complex detection
Farashi, S. (2015) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2015.09.008 · Cited by 54
Parametric or nonparametric? A parametricness index for model selection
Liu, W., & Yang, Y. (2011) · The Annals of Statistics · doi:10.1214/11-aos899 · Cited by 55
Complexity regularization via localized random penalties
Lugosi, G., & Wegkamp, M. (2004) · The Annals of Statistics · doi:10.1214/009053604000000463 · Cited by 55
Self-citationDougherty, E. R., & Braga-Neto, U. (2006) · Journal of Biological Systems · doi:10.1142/s0218339006001726 · Cited by 55
Steinwart, I., Hush, D. R., & Scovel, C. (2009) · University of North Texas Digital Library (University of North Texas) · Cited by 55
Graph Classification Using Signal-Subgraphs: Applications in Statistical Connectomics
Vogelstein, J. T., Roncal, W. G., Vogelstein, R. J., & Priebe, C. E. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.235 · Cited by 55
Consistent multilabel classification
Koyejo, S., Ravikumar, P., Natarajan, N., & Dhillon, I. S. (2015) · neural information processing systems · Cited by 55
de Mello, R. F., & Ponti, M. A. (2018) · Springer eBooks · doi:10.1007/978-3-319-94989-5_2 · Cited by 58
Achievable Rates for Pattern Recognition
Westover, M. B., & O’Sullivan, J. A. (2008) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.911296 · Cited by 57
Feature selection with limited datasets
Kupinski, M. A., & Giger, M. L. (1999) · Medical Physics · doi:10.1118/1.598821 · Cited by 55
Low-dimensional embedding using adaptively selected ordinal data
Jamieson, K., & Nowak, R. D. (2011) · Allerton Conference on Communication, Control, and Computing · doi:10.1109/allerton.2011.6120287 · Cited by 54
Statistical Discrimination Analysis Using the Maximum Function
Pham-Gia, T., Turkkan, N., & Vovan, T. (2008) · Communications in Statistics - Simulation and Computation · doi:10.1080/03610910701790475 · Cited by 54
Quantization and clustering with Bregman divergences
Fischer, A. (2010) · Journal of Multivariate Analysis · doi:10.1016/j.jmva.2010.05.008 · Cited by 54
Identification of low-level point radioactive sources using a sensor network
Chin, J.-C., Rao, N. S. V., Yau, D. K. Y., Shankar, M. A., Yang, Y. Y., Hou, J. C., Srinivasagopalan, S., & Iyengar, S. (2010) · ACM Transactions on Sensor Networks · doi:10.1145/1807048.1807050 · Cited by 54
Mavroforakis, M., Georgiou, H., Dimitropoulos, N., Cavouras, D. A., & Theodoridis, S. (2005) · European Journal of Radiology · doi:10.1016/j.ejrad.2004.12.015 · Cited by 54
Zhou, Y., Zhang, R., Wang, S., & Wang, F. (2018) · Sensors · doi:10.3390/s18072013 · Cited by 55
Chacón, J. E. (2020) · International Statistical Review · doi:10.1111/insr.12340 · Cited by 58
On the tight constant in the multivariate Dvoretzky–Kiefer–Wolfowitz inequality
Naaman, M. (2021) · Statistics & Probability Letters · doi:10.1016/j.spl.2021.109088 · Cited by 63
Content-based retrieval from image databases: current solutions and future directions
Vasconcelos, N. M., & Kunt, M. (2002) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2001.958037 · Cited by 54
Properties of Bagged Nearest Neighbour Classifiers
Hall, P., & Samworth, R. J. (2005) · Journal of the Royal Statistical Society Series B (Statistical Methodology) · doi:10.1111/j.1467-9868.2005.00506.x · Cited by 54
Stability of Density-Based ClusteringPreprint
Rinaldo, A., Singh, A., Nugent, R., & Wasserman, L. (2010) · arXiv (Cornell University) · doi:10.1184/r1/6476357 · Cited by 54
Statistical Learning in Multiple Instance Problems
Xu, X. (2003) · Research Commons (University of Waikato) · Cited by 54
Learning from binary labels with instance-dependent noise
Menon, A. K., van Rooyen, B., & Natarajan, N. (2018) · Machine Learning · doi:10.1007/s10994-018-5715-3 · Cited by 53
COVID-19 vaccine hesitancy: a social media analysis using deep learning
Nyawa, S., Tchuente, D., & Wamba, S. F. (2022) · Annals of Operations Research · doi:10.1007/s10479-022-04792-3 · Cited by 55
Time-integrated BMP signaling determines fate in a stem cell model for early human development
Teague, S., Primavera, G., Chen, B., Liu, Z.-Y., Yao, L., Freeburne, E., Khan, H., Jo, K., Johnson, C. N., & Heemskerk, I. (2024) · Nature Communications · doi:10.1038/s41467-024-45719-9 · Cited by 58
Quantum learning: asymptotically optimal classification of qubit states
Guţǎ, M., & Kotłowski, W. (2010) · New Journal of Physics · doi:10.1088/1367-2630/12/12/123032 · Cited by 54
Decorrelation of the True and Estimated Classifier Errors in High-Dimensional Settings
Hanczar, B., Jianping Hua, A., & Dougherty, E. R. (2007) · EURASIP Journal on Bioinformatics and Systems Biology · doi:10.1155/2007/38473 · Cited by 53
Miller, M. I., Priebe, C. E., Qiu, A., Fischl, B. R., Kolasny, A., Brown, T., Park, Y., Ratnanather, J. T., Busa, E., Jovicich, J., et al. (2008) · Human Brain Mapping · doi:10.1002/hbm.20655 · Cited by 54
Adaptive Kernel Methods Using the Balancing Principle
Vito, E., Pereverzyev, S., & Rosasco, L. (2010) · Foundations of Computational Mathematics · doi:10.1007/s10208-010-9064-2 · Cited by 66
PAC-Bayesian Analysis of Co-clustering and Beyond
Seldin, Y., & Tishby, N. (2010) · Max Planck Institute for Plasma Physics · doi:10.5555/1756006.1953046 · Cited by 53
Georgiou, H., Mavroforakis, M., Dimitropoulos, N., Cavouras, D., & Theodoridis, S. (2007) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2007.06.004 · Cited by 53
Scalable discriminant feature selection for image retrieval and recognition
Vasconcelos, N. M., & Vasconcelos, M. (2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315242 · Cited by 53
Adaptive model selection using empirical complexities
Lugosi, G., & Nobel, A. B. (1999) · The Annals of Statistics · doi:10.1214/aos/1017939241 · Cited by 53
Self-citationBayesian relevance feedback for content-based image retrieval
Vasconcelos, N. M., & Lippman (2000) · IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL) · doi:10.1109/ivl.2000.853841 · Cited by 53
High-Dimensional Non-Linear Variable Selection through Hierarchical Kernel LearningPreprint
Bach, F. (2009) · arXiv (Cornell University) · doi:10.48550/arxiv.0909.0844 · Cited by 57
Machine learning \& artificial intelligence in the quantum domainPreprint
Dunjko, V., & Briegel, H. J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1709.02779 · Cited by 54
On the Design of Federated Learning in the Mobile Edge Computing Systems
Feng, C., Zhao, Z., Wang, Y., Quek, T. Q. S., & Peng, M. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3087125 · Cited by 55
Bias in Estimating the Variance of K-Fold Cross-Validation
Bengio, Y., & Grandvalet, Y. (2005) · Springer eBooks · doi:10.1007/0-387-24555-3_5 · Cited by 52
Generalization Error Bounds Using Unlabeled Data
Kääriäinen, M. (2005) · Lecture notes in computer science · doi:10.1007/11503415_9 · Cited by 52
On the Generalization Ability of On-line Learning Algorithms
Cesa‐Bianchi, N., Conconi, A., & Gentile, C. (2002) · The MIT Press eBooks · doi:10.7551/mitpress/1120.003.0051 · Cited by 55
On the Use of Reproducing Kernel Hilbert Spaces in Functional Classification
Berrendero, J. R., Cuevas, A., & Torrecilla, J. L. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1320287 · Cited by 52
Universal prediction of individual binary sequences in the presence of noise
Weissman, T., & Merhav, N. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.945240 · Cited by 52
Neyman-Pearson classification, convexity and stochastic constraintsPreprint
Rigollet, P., & Tong, X. (2011) · arXiv (Cornell University) · doi:10.48550/arxiv.1102.5750 · Cited by 52
Pal, R. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-805274-7.00004-x · Cited by 51
Soldera, J., Behaine, C. A. R., & Scharcanski, J. (2015) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2015.2415012 · Cited by 51
Approximation by neural networks and learning theory
Maiorov, V. (2005) · Journal of Complexity · doi:10.1016/j.jco.2005.09.001 · Cited by 51
Statistical learning theory: a tutorial
Kulkarni, S. R., & Harman, G. H. (2011) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.179 · Cited by 51
Optimization by Gradient Boosting
Gérard Biau, A., & Benoît Cadre, B. (2021) · Springer eBooks · doi:10.1007/978-3-030-73249-3_2 · Cited by 56
Machine Learning Approaches for Epidemiological Investigations of Food-Borne Disease Outbreaks
Vilne, B., Meistere, I., Grantiņa-Ieviņa, L., & Ķibilds, J. (2019) · Frontiers in Microbiology · doi:10.3389/fmicb.2019.01722 · Cited by 54
Combining Classifiers via Discretization
Mojirsheibani, M. (1999) · Journal of the American Statistical Association · doi:10.1080/01621459.1999.10474154 · Cited by 51
Superior feature-set ranking for small samples using bolstered error estimation
Chao Sima, A., Braga-Neto, U., & Dougherty, E. R. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bti081 · Cited by 51
Learning without concentration for general loss functions
Mendelson, S. (2017) · Probability Theory and Related Fields · doi:10.1007/s00440-017-0784-y · Cited by 50
Comparing distributions and shapes using the kernel distance
Joshi, S. C., Kommaraju, R. V., Phillips, J. M., & Venkatasubramanian, S. (2011) · Annual Symposium on Computational Geometry (SoCG) · doi:10.1145/1998196.1998204 · Cited by 52
Lower Bounds for the Empirical Minimization Algorithm
Mendelson, S. (2008) · IEEE Transactions on Information Theory · doi:10.1109/tit.2008.926323 · Cited by 50
Nonparametric regression estimation using penalized least squares
Köhler, M., & Krzyżak, A. (2001) · IEEE Transactions on Information Theory · doi:10.1109/18.998089 · Cited by 50
Decision Makers as Statisticians: Diversity, Ambiguity, and Learning
Al‐Najjar, N. I. (2009) · Econometrica · doi:10.3982/ecta7501 · Cited by 50
Principal curves with bounded turn
Sandilya, S., & Kulkarni, S. R. (2002) · IEEE Transactions on Information Theory · doi:10.1109/tit.2002.802614 · Cited by 50
Principal curves: learning, design, and applications
Krzyżak, A., & Kégl, B. (2000) · Cited by 50
Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes
Liu, T., Tao, D., & Xu, D. (2016) · Neural Computation · doi:10.1162/neco_a_00872 · Cited by 49
Road Sign Classification without Color Information
Paclı́k, P., & Novovičová, J. (2000) · Cited by 49
Taneja, S., Akçamete, A., Akinci, B., Garrett, J. H., Soibelman, L., & East, E. W. (2012) · Journal of Computing in Civil Engineering · doi:10.1061/(asce)cp.1943-5487.0000177 · Cited by 49
Laser Doppler Vibrometry Measures of Physiological Function: Evaluation of Biometric Capabilities
Chen, M., O’Sullivan, J. A., Singla, N., Sirevaag, E. J., Kristjansson, S. D., Lai, P., Kaplan, A. D., & Rohrbaugh, J. (2010) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2010.2051542 · Cited by 49
On the Size and Recovery of Submatrices of Ones in a Random Binary Matrix
Nobel, A. B., & Sun, X. (2021) · Carolina Digital Repository (University of North Carolina at Chapel Hill) · doi:10.17615/ss7d-qe88 · Cited by 45
Active Learning in Multi-armed Bandits
Antos, A., Grover, V., & Szepesvári, C. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-87987-9_25 · Cited by 49
Machine Learning in Bioinformatics
Calvo, B., Santana, R., Bielza, C., Galdiano, J., Lozano, J. A., & Victor Robles, E. A. (2008) · Wiley eBooks · doi:10.1002/9780470397428 · Cited by 49
Complexities of convex combinations and bounding the generalization error in classification
Koltchinskii, V. I., & Panchenko, D. (2005) · The Annals of Statistics · doi:10.1214/009053605000000228 · Cited by 49
Improved Risk Tail Bounds for On-Line Algorithms
Cesa‐Bianchi, N., & Gentile, C. (2008) · IEEE Transactions on Information Theory · doi:10.1109/tit.2007.911292 · Cited by 49
A multiresolution manifold distance for invariant image similarity
Vasconcelos, N. M., & Lippman, A. (2005) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2004.840596 · Cited by 49
k-Nearest Neighbors for automated classification of celestial objects
Li, L., Zhang, Y., & Zhao, Y. (2008) · Science in China. Series G, Physics, mechanics & astronomy/Science in China. Series G, Physics, Mechanics & Astronomy · doi:10.1007/s11433-008-0088-4 · Cited by 51
Novel opportunities in automated classification of antinuclear antibodies on HEp-2 cells
Rigon, A., Buzzulini, F., Soda, P., Onofri, L., Arcarese, L., Iannello, G., & Afeltra, A. (2011) · Autoimmunity Reviews · doi:10.1016/j.autrev.2011.04.022 · Cited by 48
A new test for randomness and its application to some cryptographic problems
Ryabko, B. Y., Stognienko, V. S., & Shokin, Y. I. (2003) · Journal of Statistical Planning and Inference · doi:10.1016/s0378-3758(03)00149-6 · Cited by 48
Statistical Inference on Errorfully Observed Graphs
Priebe, C. E., Sussman, D. L., Tang, M., & Vogelstein, J. T. (2014) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2014.951049 · Cited by 45
Asymptotic Properties of a Nonparametric Regression Function Estimator with Randomly Truncated Data
Ould‐Saïd, E., & Lemdani, M. (2006) · Annals of the Institute of Statistical Mathematics · doi:10.1007/s10463-005-0011-y · Cited by 48
Minimax Estimation of the $L_{1}$ Distance
Jiao, J., Han, Y., & Weissman, T. (2018) · IEEE Transactions on Information Theory · doi:10.1109/tit.2018.2846245 · Cited by 48
Estimation of soybean leaf area, edge, and defoliation using color image analysis
Liang, W.-Z., Kirk, K. R., & Greene, J. K. (2018) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2018.03.021 · Cited by 48
Parameter Convergence and Learning Curves for Neural Networks
Fine, T. L., & Mukherjee, S. (1999) · Neural Computation · doi:10.1162/089976699300016647 · Cited by 48
Formal methods in pattern recognition: A review
Nieddu, L., & Patrizi, G. (2000) · European Journal of Operational Research · doi:10.1016/s0377-2217(98)00368-3 · Cited by 48
On the Kernel Rule for Function Classification
Abraham, C., Gérard Biau, A., & Benoît Cadre, B. (2006) · Annals of the Institute of Statistical Mathematics · doi:10.1007/s10463-006-0032-1 · Cited by 48
PREDICTION/ESTIMATION WITH SIMPLE LINEAR MODELS: IS IT REALLY THAT SIMPLE?
Yang, Y. (2006) · Econometric Theory · doi:10.1017/s0266466607070016 · Cited by 48
Progressive mixture rules are deviation suboptimal
Audibert, J.-Y. (2007) · Cited by 48
Algoet, P. (1999) · IEEE Transactions on Information Theory · doi:10.1109/18.761258 · Cited by 48
A new method for GAN-based data augmentation for classes with distinct clusters
Kuntalp, M., & Düzyel, O. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121199 · Cited by 47
A bound on the deviation probability for sums of non-negative random variables.
Maurer, A. (2003) · Cited by 47
Mixture Subclass Discriminant Analysis Link to Restricted Gaussian Model and Other Generalizations
Gkalelis, N., Mezaris, V., Kompatsiaris, I. Y., & Stathaki, T. (2012) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2012.2216545 · Cited by 47
Citation count prediction as a link prediction problem
Pobiedina, N., & Ichise, R. (2015) · Applied Intelligence · doi:10.1007/s10489-015-0657-y · Cited by 48
Low-Density Graph Codes That Are Optimal for Binning and Coding With Side Information
Wainwright, M. J., & Martinian, E. (2009) · IEEE Transactions on Information Theory · doi:10.1109/tit.2008.2009815 · Cited by 47
Bicego, M., Tessari, G., Tecchiolli, G., & Bettinelli, M. (2002) · Sensors and Actuators B Chemical · doi:10.1016/s0925-4005(02)00065-5 · Cited by 47
Metadata
- Authors
- Devroye, L., Györfi, L., & Lugosi, G.
- Affiliations
- McGill University (CA)
- Budapest University of Technology and Economics (HU)
- Venue
- Stochastic Modelling and Applied Probability
- Publisher
- Springer New York
- Published
- 1996
- ISSN
- 2197-4381 (ISSN-L)
- 0172-4568
- 2197-439X
- OpenAlex
- W1564947197
- Cited by
- 3,274
- References
- 6
- Citations by year
- 2026: 31 · 2025: 57 · 2024: 74 · 2023: 69 · 2022: 101 · 2021: 113 · 2020: 131 · 2019: 136 · 2018: 106 · 2017: 98 · 2016: 120 · 2015: 150 · 2014: 125 · 2013: 119 · 2012: 141
- FWCI
- 20.89
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Stochastic modelling and applied probability (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Keywords
- distance measures, kernel methods, nearest neighbors, Vapnik-Chervonenkis theory, epsilon-entropy, parametric classification, error estimation, classification trees, artificial neural networks
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
