A modified finite newton method for fast solution of large scale linear svms (2005)
| Venue: | Journal of Machine Learning Research |
| Citations: | 57 - 7 self |
BibTeX
@ARTICLE{Keerthi05amodified,
author = {S. Sathiya Keerthi and Dennis Decoste and Thorsten Joachims},
title = {A modified finite newton method for fast solution of large scale linear svms},
journal = {Journal of Machine Learning Research},
year = {2005},
volume = {6},
pages = {2005}
}
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Abstract
This paper develops a fast method for solving linear SVMs with L2 loss function that is suited for large scale data mining tasks such as text classification. This is done by modifying the finite Newton method of Mangasarian in several ways. Experiments indicate that the method is much faster than decomposition methods such as SVM light, SMO and BSVM (e.g., 4-100 fold), especially when the number of examples is large. The paper also suggests ways of extending the method to other loss functions such as the modified Huber’s loss function and the L1 loss function, and also for solving ordinal regression.
