The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Improvements to the SMO algorithm for SVM regression
Abstract
This paper points out an important source of inefficiency in Smola and Schölkopf's sequential minimal optimization (SMO) algorithm for support vector machine (SVM) regression that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO for regression. These modified algorithms perform significantly faster than the original SMO on the datasets tried.
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Research works printed but not linked: 10
- mangasarian, 1999, Massive Support Vector Regression
- 0, Delve Data for Evaluating Learning in Valid Experiments
- keerthi, 1999, Improvements to Platt s SMO Algorithm for SVM Classifier Design Accepted for Publication in Neural Computation
- smola, 1998, A tutorial on support vector regression
- 10.1109/72.822516 · DOI · Paper page
- shevade, 1999, Improvements to SMO Algorithm for Regression
- platt, 1998, Advances in Kernel Methods Support Vector Machines
- burges, 1998, a tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, 3
- smola, 1998, Learning with kernels
- blake, 1998, UCI repository of machine learning databases
Cited by
Pires, D. E. V., Ascher, D. B., & Blundell, T. L. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku411 · Cited by 932
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Berecibar, M., Devriendt, F., Dubarry, M., Villarreal, I., Omar, N., Verbeke, W., & Van Mierlo, J. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.04.109 · Cited by 202
Evaluating Segmentation Error without Ground Truth
Kohlberger, T., Singh, V., Alvino, C., Bahlmann, C., & Grady, L. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33415-3_65 · Cited by 102
Learning Spatiotemporal Failure Dependencies for Resilient Edge Computing Services
Aral, A., & Brandic, I. (2021) · IEEE Transactions on Parallel and Distributed Systems · doi:10.1109/tpds.2020.3046188 · Cited by 68
Emotion Recognition and Depression Diagnosis by Acoustic and Visual Features: A Multimodal Approach
Sidorov, M., & Minker, W. (2014) · Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge · doi:10.1145/2661806.2661816 · Cited by 56
Metadata
- Authors
- Shevade, S. K., Keerthi, S. S., Bhattacharyya, C., & Murthy, K. R. K.
- Venue
- IEEE Transactions on Neural Networks
- Published
- 2000
- Volume
- 11
- Issue
- 5
- Pages
- 1188-1193
- ISSN
- 1045-9227 (ISSN-L)
- OpenAlex
- W2122284941
- Cited by
- 858
- References
- 1
- FWCI
- 5.83
- Type
- journal article
- Language
- en
- Open access
- closed
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