Robust multiple-instance learning ensembles using random subspace instance selection
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The Hopfield approach outperformed other methods on the Tiger, Elephant and UCSB Breast Cancer MIL datasets.
“Our approach has set a new state-of-the-art and has outperformed other methods (Küçüka¸ scı & Baydo˘ gan, 2018; Carbonneau et al., 2016) on the datasets Tiger, Elephant and UCSB Breast Cancer (see Table 1).”
From Hopfield Networks is All You Need (2020) · cites this paper as Carbonneau et al., 2016
The Hopfield approach set a new state-of-the-art on the Tiger, Elephant and UCSB Breast Cancer MIL datasets.
“Our approach has set a new state-of-the-art and has outperformed other methods (Küçüka¸ scı & Baydo˘ gan, 2018; Carbonneau et al., 2016) on the datasets Tiger, Elephant and UCSB Breast Cancer (see Table 1).”
From Hopfield Networks is All You Need (2020) · cites this paper as Carbonneau et al., 2016
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References 37 · Cited by 60
- Authors
- Carbonneau, M.-A., Granger, E., Raymond, A. J., & Gagnon, G.
- Affiliations
- Laboratoire de communications et d'intégration de la microélectronique (CA)
- Corresponding author
- Carbonneau, Marc-André
- Venue
- Pattern Recognition
- Publisher
- Elsevier BV
- Published
- 2016-10
- Volume
- 58
- Pages
- 83-99
- ISSN
- 0031-3203 (ISSN-L)
- OpenAlex
- W2336727000
- Cited by
- 60
- References
- 37
- Citations by year
- 2026: 1 · 2025: 5 · 2024: 5 · 2023: 5 · 2022: 6 · 2021: 11 · 2020: 8 · 2019: 1 · 2018: 5 · 2017: 10 · 2016: 2 · 2014: 1
- FWCI
- 4.94
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Pattern Recognition (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- multiple instance learning, ensemble learning, instance selection, ensemble classifiers, noise robustness, noisy features
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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.
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