Bias-variance tradeoff in hybrid generative-discriminative models
Abstract
Given any generative classifier based on an inexact density model, we can define a discriminative counterpart that reduces its asymptotic error rate, while increasing the estimation variance. An optimal bias-variance balance might be found using hybrid generative-discriminative (HGD) approaches. In these paper, these methods are defined in a unified framework. This allow us to find sufficient conditions under which an improvement in generalization performances is guaranteed. Numerical experiments illustrate the well fondness of our statements.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Cites
Pattern Recognition and Neural Networks
Ripley, B. D. (1996) · Cambridge University Press eBooks · doi:10.1017/cbo9780511812651 · Cited by 6,446 · see citing works
Text classification from labeled and unlabeled documents using EM
Nigam, K., Mccallum, A. K., Thrun, S., & Mitchell, T. (2000) · Machine Learning · doi:10.1023/a:1007692713085 · Cited by 2,736 · see citing works
(2001) · Cited by 1,932 · see citing works
Exploiting Generative Models in Discriminative Classifiers
Jaakkola, T., & Haussler, D. H. (1998) · Cited by 1,274 · see citing works
Principled Hybrids of Generative and Discriminative Models
Lasserre, J., Bishop, C. M., & Minka, T. P. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.227 · Cited by 341 · see citing works
Classification with Hybrid Generative/Discriminative Models
Raina, R., Shen, Y., McCallum, A., & Ng, A. Y. (2003) · ScholarWorks@UMassAmherst (University of Massachusetts Amherst) · Cited by 201 · see citing works
The Tradeoff Between Generative and Discriminative Classifiers
Bouchard, G., & Triggs, B. (2004) · Cited by 114 · see citing works
Self-citationA hybrid generative/discriminative approach to semisupervised classifier design
Fujino, A., Ueda, N., & Saito, K. (2005) · Cited by 105 · see citing works
Greiner, R., & Zhou, W. J. (2002) · doi:10.5555/777092.777121 · Cited by 87 · see citing works
O’Neill, T. J. (1980) · Journal of the American Statistical Association · doi:10.1080/01621459.1980.10477446 · Cited by 47 · see citing works
Using Augmented Statistical Models and Score Spaces for Classification
Smith, N. D. (2003) · Medical Entomology and Zoology · Cited by 23 · see citing works
Kelm, B. M., Pal, C., & McCallum, A. (2006) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2006.384 · Cited by 20 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Koçak, B., Ponsiglione, A., Stanzione, A., Bluethgen, C., Santinha, J., Ugga, L., Huisman, M., Klontzas, M. E., Cannella, R., & Cuocolo, R. (2024) · Diagnostic and Interventional Radiology · doi:10.4274/dir.2024.242854 · Cited by 168 · see citing works
- Authors
- Bouchard, G.
- Corresponding author
- Bouchard, Guillaume
- ORCID
- Bouchard, Guillaume
- Venue
- Sixth International Conference on Machine Learning and Applications (ICMLA 2007)
- Publisher
- IEEE
- Published
- 2007-12
- Pages
- 124-129
- OpenAlex
- W3147467675
- Cited by
- 16
- References
- 12
- Citations by year
- 2025: 1 · 2024: 4 · 2022: 1 · 2018: 1 · 2016: 2 · 2013: 1 · 2012: 2
- FWCI
- 0.65
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Machine Learning and Applications (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- bias-variance trade-off, hybrid generative-discriminative models, generative classifiers, discriminative classifiers, asymptotic error rate, estimation variance, generalization performance
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 ›
Are you an author, or do you see a problem with this page? Request a review
