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Large Margin Rank Boundaries for Ordinal Regression
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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
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
Training linear SVMs in linear time
Joachims, T. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150429 · Cited by 1,920
Using linguistic cues for the automatic recognition of personality in conversation and text
Mairesse, F., Walker, M. A., Mehl, M. R., & Moore, R. K. (2007) · Journal of Artificial Intelligence Research · doi:10.1613/jair.2349 · Cited by 1,046
A Theoretical Analysis of NDCG Type Ranking Measures
Wang, Y., Wang, L., Li, Y., He, D., Liu, T.-Y., & Chen, W. (2013) · arXiv · doi:10.48550/arxiv.1304.6480 · Cited by 351
An Introduction to Neural Information Retrieval
Mitra, B., & Craswell, N. (2018) · Foundations and Trends® in Information Retrieval · doi:10.1561/1500000061 · Cited by 317
Automatic Chinese Factual Question Generation
Liu, M., Rus, V., & Liu, L. (2017) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2016.2565477 · Cited by 52
Metadata
- Authors
- Herbrich, R., Graepel, T., & Obermayer, K.
- Venue
- Advances in Large-Margin Classifiers
- Publisher
- The MIT Press
- Published
- 2000-09-29
- Pages
- 115-132
- OpenAlex
- W2988119488
- Cited by
- 1,074
- References
- 0
- FWCI
- 9.60
- Type
- book chapter
- Language
- en
- Open access
- closed
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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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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.
