The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Confidence-weighted linear classification
Abstract
We introduce confidence-weighted linear classifiers, which add parameter confidence information to linear classifiers. Online learners in this setting update both classifier parameters and the estimate of their confidence. The particular online algorithms we study here maintain a Gaussian distribution over parameter vectors and update the mean and covariance of the distribution with each instance. Empirical evaluation on a range of NLP tasks show that our algorithm improves over other state of the art online and batch methods, learns faster in the online setting, and lends itself to better classifier combination after parallel training.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Boyd, S., & Vandenberghe, L. (2004) · doi:10.1017/cbo9780511804441 · Cited by 56,601
LIBSVM: A library for support vector machines
Chang, C.-C., & Lin, C.-J. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1961189.1961199 · Cited by 40,918
The perceptron: A probabilistic model for information storage and organization in the brain.
Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280
Gaussian Processes for Machine Learning
Rasmussen, C. E., & Williams, C. K. I. (2005) · doi:10.7551/mitpress/3206.001.0001 · Cited by 11,705
RCV1: A New Benchmark Collection for Text Categorization Research
Lewis, D., Yang, Y., Rose, T. G., & Li, F. (2004) · Goldsmiths (University of London) · doi:10.5555/1005332.1005345 · Cited by 2,503
(2007) · Cited by 2,018
Online Passive-Aggressive Algorithms
Crammer, K., Dekel, O., Keshet, J., Shalev‐Shwartz, S., & Singer, Y. (2003) · Cited by 1,682
Self-citationPetersen, K. B., & Pedersen, M. S. (2012) · doi:10.5281/zenodo.8002926 · Cited by 1,434
A Second-Order Perceptron Algorithm
Cesa-Bianchi, N., Conconi, A., & Gentile, C. (2005) · SIAM Journal on Computing · doi:10.1137/s0097539703432542 · Cited by 196
Adapting bias by gradient descent: an incremental version of delta-bar-delta
Sutton, R. S. (1992) · Cited by 171
Cited by
Building Watson: An Overview of the DeepQA Project
Ferrucci, D., Brown, E., Chu‐Carroll, J., Fan, J., Gondek, D., Kalyanpur, A. A., Lally, A., Murdock, J. W., Nyberg, E., Prager, J., et al. (2010) · AI Magazine · doi:10.1609/aimag.v31i3.2303 · Cited by 1,497
Online learning: A comprehensive survey
Hoi, S. C. H., Sahoo, D., Lu, J., & Zhao, P. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.04.112 · Cited by 746
Identifying suspicious URLs: an application of large-scale online learning
Ma, J., Saul, L. K., Savage, S., & Voelker, G. M. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553462 · Cited by 498
Adaptive bound optimization for online convex optimization
McMahan, H. B., & Streeter, M. (2010) · arXiv · doi:10.48550/arxiv.1002.4908 · Cited by 227
Say Anything: Using Textual Case-Based Reasoning to Enable Open-Domain Interactive Storytelling
Swanson, R., & Gordon, A. S. (2012) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/2362394.2362398 · Cited by 85
Metadata
- Authors
- Dredze, M., Crammer, K., & Pereira, F.
- Venue
- Proceedings of the 25th international conference on Machine learning - ICML '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 264-271
- OpenAlex
- W2122537498
- Cited by
- 375
- References
- 12
- FWCI
- 16.62
- Type
- conference paper
- Language
- en
- Open access
- closed
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Confidence-weighted linear classification. https://app.cochat.ai/papers/10.1145/1390156.1390190
- The paper
- Dredze, M., Crammer, K., & Pereira, F. (2008). Confidence-weighted linear classification. Proceedings of the 25th international conference on Machine learning - ICML '08. https://doi.org/10.1145/1390156.1390190
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
