The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Support vector learning for ordinal regression
Abstract
We investigate the problem of predicting variables of ordinal scale. This taks is referred to as ordinal regression and is complementary to the standard machine learning tasks of classification and metric regression. In contrast to statistical models we present a distribution independent formulation of the problem together with uniform bounds of the risk functional. The approach presented is based on a mapping from objects to scalar utility values. Similar to Support Vector methods we derive a new learning algorithm for the task of ordinal regression based on large margin rank boundaries. We give experimental results for an information retrieval task: learning the order of documents w.r.t. an initial query. Experimental results indicate that the presented algorithm outperforms more naive approaches to ordinal regression such as Support Vector classification and Support Vector regression in the case of more than two ranks. 1 Introduction Problems of ordinal regression arise in many fi...
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Multiclass Support Vector Machines
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Structural risk minimization over data-dependent hierarchies
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Support vector machines applied to face recognition
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Cited by
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
Sampling Matters in Deep Embedding Learning
Wu, C.-Y., Manmatha, R., Smola, A. J., & Krahenbuhl, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.309 · Cited by 836
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
Incremental support vector learning for ordinal regression
Gu, B., Sheng, V. S., Tay, K. Y., Romano, W., & Li, S. (2015) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2014.2342533 · Cited by 707
Metadata
- Authors
- Herbrich, R.
- Venue
- 9th International Conference on Artificial Neural Networks: ICANN '99
- Publisher
- IEE
- Published
- 1999
- Volume
- 1999
- Pages
- 97-102
- OpenAlex
- W2058475745
- Cited by
- 448
- References
- 7
- FWCI
- 0.85
- Type
- conference paper
- Language
- en
- Open access
- closed
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