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Cited by 491Neural Networks and ApplicationsStochastic Gradient Optimization TechniquesMachine Learning and ELM

The DOI matches its Crossref record; 13 of the 14 references it cites were found in a registry or an index.

Overfitting and neural networks: conjugate gradient and backpropagation

Lawrence, S., Giles, C.L.·2000·Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium
DOI 10.1109/ijcnn.2000.857823
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Abstract

Methods for controlling the bias/variance tradeoff typically assume that overfitting or overtraining is a global phenomenon. For multi-layer perceptron (MLP) neural networks, global parameters such as the training time, network size, or the amount of weight decay are commonly used to control the bias/variance tradeoff. However, the degree of overfitting can vary significantly throughout the input space of the model. We show that overselection of the degrees of freedom for an MLP trained with backpropagation can improve the approximation in regions of underfitting, while not significantly overfitting in other regions. This can be a significant advantage over other models. Furthermore, we show that "better" learning algorithms such as conjugate gradient can in fact lead to worse generalization, because they can be more prone to creating varying degrees of overfitting in different regions of the input space. While experimental results can not cover all practical situations, our results do help to explain common behavior that does not agree with theoretical expectations. Our results suggest one important reason for the relative success of MLPs, bring into question common beliefs about neural network training regarding training algorithms, overfitting, and optimal network size, suggest alternate guidelines for practical use, and help to direct future work.

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Research works printed but not linked: 4

  1. darken, 1991, Advances in neural information processing systems, 3, 832
  2. weigend, 1991, Advances in neural information processing systems, 3, 875
  3. le cun, 1990, Advances in neural information processing systems, 2, 598
  4. vapnik, 1982, Estimation of Dependencies Based on Empirical Data

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Metadata

Authors
Lawrence, S., & Giles, C. L.
Affiliations
NEC (Japan) (JP)
ORCID
Giles, C.L.
Venue
Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium
Publisher
IEEE
Published
2000
Pages
114-119 vol.1
ISSN
2161-4407 (ISSN-L)
DOI
10.1109/ijcnn.2000.857823
OpenAlex
W2114367267
Cited by
491
References
14
Citations by year
2026: 11 · 2025: 32 · 2024: 41 · 2023: 42 · 2022: 51 · 2021: 67 · 2020: 60 · 2019: 44 · 2018: 23 · 2017: 14 · 2016: 6 · 2015: 8 · 2014: 10 · 2013: 10 · 2012: 8
FWCI
2.54
Type
conference paper
Language
en
Open access
closed
Locations
International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
Keywords
overfitting, bias-variance trade-off, multilayer perceptron, conjugate gradient method, backpropagation, generalization, learning algorithms, network size, weight decay, input space

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