The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Recent Advances of Large-Scale Linear Classification
Abstract
Linear classification is a useful tool in machine learning and data mining. For some data in a rich dimensional space, the performance (i.e., testing accuracy) of linear classifiers has shown to be close to that of nonlinear classifiers such as kernel methods, but training and testing speed is much faster. Recently, many research works have developed efficient optimization methods to construct linear classifiers and applied them to some large-scale applications. In this paper, we give a comprehensive survey on the recent development of this active research area.
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Cited by
Applications of deep learning and reinforcement learning to biological data
Mahmud, M., Kaiser, M. S., Hussain, A., & Vassanelli, S. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2790388 · Cited by 765
Richtárik, P., & Takáč, M. (2014) · Mathematical Programming · doi:10.1007/s10107-012-0614-z · Cited by 654
Qi, W., Ovur, S. E., Li, Z., Marzullo, A., & Song, R. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3089999 · Cited by 253
Large linear classification when data cannot fit in memory
Yu, H.-F., Hsieh, C.-J., Chang, K.-W., & Lin, C.-J. (2012) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2086737.2086743 · Cited by 69
Self-citationFast flux discriminant for large-scale sparse nonlinear classification
Chen, W., Chen, Y., & Weinberger, K. Q. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623627 · Cited by 12
Metadata
- Authors
- Yuan, G.-X., Ho, C.-H., & Lin, C.-J.
- Venue
- Proceedings of the IEEE
- Published
- 2012-09
- Volume
- 100
- Issue
- 9
- Pages
- 2584-2603
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2028870622
- Cited by
- 266
- References
- 10
- FWCI
- 26.60
- Type
- journal article
- Language
- en
- Open access
- closed
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