IEEE Transactions on Neural Networks
regression, active set, support vector
We present ASVR, a new active set strategy to solve a straightforward reformulation of the standard support vector regression problem. This new algorithm is based on the successful ASVM algorithm for classification problems, and consists of solving a finite number of linear equations with a typically large dimensionality equal to the number of points to be approximated. However, by making use of the Sherman-Morrison-Woodbury formula, a much smaller matrix of the order of the original input space is inverted at each step. The algorithm requires no specialized quadratic or linear programming code, but merely a linear equation solver which is publicly available. ASVR is extremely fast, produces comparable generalization error to other popular algorithms, and is available on the web for download.
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D. R. Musicant and A. Feinberg, "Active Set Support Vector Regression," IEEE Transactions on Neural Networks, vol. 15, no. 2, pp. 268-275. Available at: https://doi.org/10.1109/TNN.2004.824259. , IEEE, Jan 2004. Accessed via Faculty Work. Computer Science. Carleton Digital Commons. https://digitalcommons.carleton.edu/cs_faculty/3
The definitive version is available at https://doi.org/10.1109/TNN.2004.824259