Cited by 267Neural Networks and ApplicationsMachine Learning and ELMStochastic Gradient Optimization Techniques

Use of some sensitivity criteria for choosing networks with good generalization ability

Dimopoulos, Yannis, Bourret, Paul, Lek, Sovan·1995·Neural Processing Letters

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Crossref · 10.1007/bf02309007

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8 of 8 references verified0 citation errors · 0 not checkable

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  • Linear models can be thought of as a first-order approximation to the data generating process.

    “Linear models are popular in practice, in part because they can be thought of as a first-order approximation to the data generating process.”

    From Empirical Asset Pricing via Machine Learning (2020) · cites this paper as [13]

  • The Dimopoulos, Bourret, and Lek (1995) variable importance method is not applicable to tree-based models because of nondifferentiabilities.

    “Note that, because of nondifferentiabilities in treebased models, the Dimopoulos, Bourret, and Lek (1995) method is not applicable.”

    From Empirical Asset Pricing via Machine Learning (2020) · cites this paper as Dimopoulos, Bourret, and Lek (1995)

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Authors
Dimopoulos, Y., Bourret, P., & Lek, S.
Affiliations
  • Université Toulouse III - Paul Sabatier (FR)
  • Office National d'Études et de Recherches Aérospatiales (FR)
Venue
Neural Processing Letters
Published
1995-12
Volume
2
Issue
6
Pages
1-4
ISSN
  • 1370-4621 (ISSN-L)
  • 1573-773X
OpenAlex
W1969404656
Cited by
267
References
8
Citations by year
2026: 4 · 2025: 15 · 2024: 15 · 2023: 21 · 2022: 17 · 2021: 17 · 2020: 25 · 2019: 19 · 2018: 22 · 2017: 13 · 2016: 13 · 2015: 11 · 2014: 6 · 2013: 7 · 2012: 5
FWCI
1.91
Type
journal article
Language
en
Open access
closed
Locations
Neural Processing Letters (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
  • Physical Sciences › Computer Science › Artificial Intelligence › Stochastic Gradient Optimization Techniques
Keywords
multilayer perceptron, model generalization, input perturbation, sensitivity index, network architecture selection, neural network training

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