A Probabilistic Theory of Pattern Recognition

Luc Devroye; László Györfi; Gábor Lugosi

doi:https://doi.org/10.1007/978-1-4612-0711-5

A Probabilistic Theory of Pattern Recognition

1996 · Stochastic modelling and applied probability · 3,327 citations · 10 from inside this corpus

Luc Devroye, László Györfi, Gábor Lugosi

No abstract in the source record.

10 of 10 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

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Citation edge evidence

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A Tutorial on Support Vector Machines for Pattern Recognition→A Probabilistic Theory of Pattern RecognitionLegacy edge — source detail unavailable
Gaussian Processes for Machine Learning→A Probabilistic Theory of Pattern RecognitionLegacy edge — source detail unavailable
An overview of statistical learning theory→A Probabilistic Theory of Pattern RecognitionLegacy edge — source detail unavailable
Semi-Supervised Learning→A Probabilistic Theory of Pattern RecognitionLegacy edge — source detail unavailable
A random forest guided tour→A Probabilistic Theory of Pattern RecognitionLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→A Probabilistic Theory of Pattern RecognitionLegacy edge — source detail unavailable
A gentle tutorial of the em algorithm and its application to parameter estimation for Gau…→A Probabilistic Theory of Pattern RecognitionLegacy edge — source detail unavailable
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results→A Probabilistic Theory of Pattern RecognitionLegacy edge — source detail unavailable
Natural Questions: A Benchmark for Question Answering Research→A Probabilistic Theory of Pattern RecognitionLegacy edge — source detail unavailable
Approximation theory of the MLP model in neural networks→A Probabilistic Theory of Pattern RecognitionLegacy edge — source detail unavailable
Semi-Supervised Learning→Rademacher and Gaussian Complexities: Risk Bounds and Structural ResultsLegacy edge — source detail unavailable
Understanding Machine Learning: From Theory To Algorithms→Rademacher and Gaussian Complexities: Risk Bounds and Structural ResultsLegacy edge — source detail unavailable

What cites it, inside the corpus

Paper	Year	Cited
A Tutorial on Support Vector Machines for Pattern Recognition	1998	16,488
Gaussian Processes for Machine Learning	2005	10,470
An overview of statistical learning theory	1999	6,311
Semi-Supervised Learning	2006	4,334
A random forest guided tour	2016	4,026
Understanding Machine Learning: From Theory To Algorithms	2015	3,081
A gentle tutorial of the em algorithm and its application to parameter estimation for Gau…	1998	2,510
Rademacher and Gaussian Complexities: Risk Bounds and Structural Results	2001	2,126
Natural Questions: A Benchmark for Question Answering Research	2019	2,095
Approximation theory of the MLP model in neural networks	1999	1,548

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Topics

Bayesian Methods and Mixture Models	Computer Science
Algorithms and Data Compression	Computer Science
Mathematical Dynamics and Fractals	Mathematics

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complete

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supports3 author record(s) attached.
supports6 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:47+00:00.

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