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A Probabilistic Theory of Pattern Recognition

Devroye, Luc, Györfi, László, Lugosi, Gábor·1996·Stochastic Modelling and Applied Probability

DOI 10.1007/978-1-4612-0711-5

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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
Probability theory
Loève, M. (1963) · Andalas University Repository (Andalas University) · Cited by 5,938
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Prewitt, K., & Durrett, R. T. (1992) · Journal of the American Statistical Association · doi:10.2307/2290301 · Cited by 3,238
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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

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Authors: Devroye, L., Györfi, L., & Lugosi, G.
Affiliations: McGill University (CA)
Budapest University of Technology and Economics (HU)
ORCID: Devroye, Luc
Györfi, László
Lugosi, Gábor
Venue: Stochastic Modelling and Applied Probability
Publisher: Springer New York
Published: 1996
ISSN: 2197-4381 (ISSN-L)
0172-4568
2197-439X
DOI: 10.1007/978-1-4612-0711-5
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

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