A Probabilistic Theory of Pattern Recognition
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The random splits procedure used to train the PFC classification unit provides a good estimate of the expected error of a classifier.
“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]
The 1-nearest neighbor regression estimate is clearly not, in general, a consistent estimate.
“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)
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- 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 ›
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