The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
A dual coordinate descent method for large-scale linear SVM
Abstract
In many applications, data appear with a huge number of instances as well as features. Linear Support Vector Machines (SVM) is one of the most popular tools to deal with such large-scale sparse data. This paper presents a novel dual coordinate descent method for linear SVM with L1-and L2-loss functions. The proposed method is simple and reaches an ε-accurate solution in O(log(1/ε)) iterations. Experiments indicate that our method is much faster than state of the art solvers such as Pegasos, TRON, SVMperf, and a recent primal coordinate descent implementation.
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Cites
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Foundations of Machine Learning
Mohri, M., Rostamizadeh, A., & Talwalkar, A. (2018) · doi:10.7551/mitpress/10290.001.0001 · Cited by 1,122
Richtárik, P., & Takáč, M. (2014) · Mathematical Programming · doi:10.1007/s10107-012-0614-z · Cited by 654
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Zhang, D., Wang, J., Cai, D., & Lu, J. (2010) · Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1835449.1835455 · Cited by 347
Towards Making Unlabeled Data Never Hurt
Li, Y.-F., & Zhou, Z.-H. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2299812 · Cited by 318
A review of optimization methodologies in support vector machines
Shawe-Taylor, J., & Sun, S. (2011) · Neurocomputing · doi:10.1016/j.neucom.2011.06.026 · Cited by 256
Metadata
- Authors
- Hsieh, C.-J., Chang, K.-W., Lin, C.-J., Keerthi, S. S., & Sundararajan, S.
- Venue
- Proceedings of the 25th international conference on Machine learning - ICML '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 408-415
- OpenAlex
- W2165966284
- Cited by
- 872
- References
- 19
- FWCI
- 30.76
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Hsieh, C.-J., Chang, K.-W., Lin, C.-J., Keerthi, S. S., & Sundararajan, S. (2008). A dual coordinate descent method for large-scale linear SVM. Proceedings of the 25th international conference on Machine learning - ICML '08. https://doi.org/10.1145/1390156.1390208
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