Abstract
Decomposition method has been widely used to efficiently solve the large size quadratic programming (QP) problems arising in support vector regression (SVR). In a decomposition method, a large QP problem is decomposed into a series of smaller QP subproblems, which can be solved much faster than the original one. In this paper, we analyze the global convergence of decomposition methods for SVR. We will show the decomposition methods for the convex programming problem formulated by Flake and Lawrence always stop within a finite number of iterations.
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Guo, J., Takahashi, N. (2008). Global Convergence Analysis of Decomposition Methods for Support Vector Regression. In: Sun, F., Zhang, J., Tan, Y., Cao, J., Yu, W. (eds) Advances in Neural Networks - ISNN 2008. ISNN 2008. Lecture Notes in Computer Science, vol 5263. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87732-5_74
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DOI: https://doi.org/10.1007/978-3-540-87732-5_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-87731-8
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