Use of some sensitivity criteria for choosing networks with good generalization ability
Integrity signals
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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)
- ORCID
- Dimopoulos, Yannis
- 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
- Licence
- www.springer.com/tdm (tdm)
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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