The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Pranking with Ranking
Abstract
We discuss the problem of ranking instances. In our framework each instance is associated with a rank or a rating, which is an integer from 1 to k. Our goal is to find a rank-prediction rule that assigns each instance a rank which is as close as possible to the instance's true rank. We describe a simple and efficient online algorithm, analyze its performance in the mistake bound model, and prove its correctness. We describe two sets of experiments, with synthetic data and with the EachMovie dataset for collaborative filtering. In the experiments we performed, our algorithm outperforms online algorithms for regression and classification applied to ranking.
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WIDROW, B., & HOFF, M. E. (1960) · doi:10.21236/ad0241531 · Cited by 4,009
Advances in Large-Margin Classifiers
(2000) · The MIT Press eBooks · doi:10.7551/mitpress/1113.001.0001 · Cited by 1,869
Large Margin Rank Boundaries for Ordinal Regression
Herbrich, R., Graepel, T., & Obermayer, K. (2000) · Advances in Large-Margin Classifiers · doi:10.7551/mitpress/1113.003.0010 · Cited by 1,074
Large margin classification using the perceptron algorithm
Freund, Y., & Schapire, R. E. (1998) · Proceedings of the eleventh annual conference on Computational learning theory · doi:10.1145/279943.279985 · Cited by 1,071
Mathematical Models in the Social Sciences.
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Ultraconservative Online Algorithms for Multiclass Problems
Crammer, K., & Singer, Y. (2001) · Lecture Notes in Computer Science · doi:10.1007/3-540-44581-1_7 · Cited by 221
Self-citation
Cited by
Learning to rank using gradient descent
Burges, C., Shaked, T., Renshaw, E., Lazier, A., Deeds, M., Hamilton, N., & Hullender, G. (2005) · Proceedings of the 22nd international conference on Machine learning - ICML '05 · doi:10.1145/1102351.1102363 · Cited by 2,836
Deep Ordinal Regression Network for Monocular Depth Estimation
Fu, H., Gong, M., Wang, C., Batmanghelich, K., & Tao, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00214 · Cited by 1,991
Learning to rank: from pairwise approach to listwise approach
Cao, Z., Qin, T., Liu, T.-Y., Tsai, M.-F., & Li, H. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273513 · Cited by 1,960
Survey on Aspect-Level Sentiment Analysis
Schouten, K., & Frasincar, F. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2015.2485209 · Cited by 832
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
Metadata
- Authors
- Crammer, K., & Singer, Y.
- Venue
- Advances in Neural Information Processing Systems 14
- Publisher
- The MIT Press
- Published
- 2002-11-08
- Pages
- 641-648
- OpenAlex
- W2171541062
- Cited by
- 538
- References
- 7
- FWCI
- 49.68
- Type
- book chapter
- Language
- en
- Open access
- closed
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