Decomposition Methods for Linear Support Vector Machines
Date Issued
2004
Date
2004
Author(s)
DOI
20060927122900179258
Abstract
In this paper, we show that decomposition methods with alpha seeding
are extremely useful for solving a sequence of linear SVMs with more data
than attributes. This strategy is motivated from (Keerthi and Lin 2003) which
proved that for an SVM with data not linearly separable, after C is large enough,
the dual solutions are at the same face. We explain why a direct use of decomposition
methods for linear SVMs is sometimes very slow and then analyze why
alpha seeding is much more effective for linear than nonlinear SVMs. We also
conduct comparisons with other methods which are efficient for linear SVMs, and
demonstrate the effectiveness of alpha seeding techniques for helping the model
selection.
Publisher
臺北市:國立臺灣大學資訊工程學系
Type
other
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