Cited by 3,578Neural Networks and ApplicationsMachine Learning and AlgorithmsMachine Learning and ELM

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Neural Networks and the Bias/Variance Dilemma

Geman, Stuart, Bienenstock, Elie, Doursat, René·1992·Neural Computation

DOI 10.1162/neco.1992.4.1.1

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Abstract

Feedforward neural networks trained by error backpropagation are examples of nonparametric regression estimators. We present a tutorial on nonparametric inference and its relation to neural networks, and we use the statistical viewpoint to highlight strengths and weaknesses of neural models. We illustrate the main points with some recognition experiments involving artificial data as well as handwritten numerals. In way of conclusion, we suggest that current-generation feedforward neural networks are largely inadequate for difficult problems in machine perception and machine learning, regardless of parallel-versus-serial hardware or other implementation issues. Furthermore, we suggest that the fundamental challenges in neural modeling are about representation rather than learning per se. This last point is supported by additional experiments with handwritten numerals.

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Authors: Geman, S., Bienenstock, E., & Doursat, R.
Affiliations: Brown University (US)
ESPCI Paris (FR)
ORCID: Doursat, René
Venue: Neural Computation
Publisher: MIT Press
Published: 1992-01
Volume: 4
Issue: 1
Pages: 1-58
ISSN: 0899-7667 (ISSN-L)
1530-888X
DOI: 10.1162/neco.1992.4.1.1
OpenAlex: W2076118331
Cited by: 3,578
References: 96
Citations by year: 2026: 125 · 2025: 131 · 2024: 115 · 2023: 128 · 2022: 148 · 2021: 201 · 2020: 181 · 2019: 142 · 2018: 126 · 2017: 97 · 2016: 115 · 2015: 99 · 2014: 103 · 2013: 82 · 2012: 110
FWCI: 60.04
Type: journal article
Language: en
Open access: closed
Locations: Neural Computation (publishedVersion)
e-space (Manchester Metropolitan University) (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
Keywords: bias-variance trade-off, feedforward neural networks, backpropagation, nonparametric regression, nonparametric inference, handwritten digit recognition, machine perception, neural network representations, statistical learning

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Neural Networks and the Bias/Variance Dilemma

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