SVMTorch: Support Vector Machines for Large-Scale Regression Problems
Ronan Collobert, Samy Bengio;
Support Vector Machines (SVMs) for regression problems are trained by solving
a quadratic optimization problem which needs on the order of l square
memory and time resources to solve, where l is the number of training
examples. In this paper, we propose a decomposition algorithm,
SVMTorch (available at http://www.idiap.ch/learning/SVMTorch.html
), which is
similar to SVM-Light proposed by Joachims (1999) for classification problems,
but adapted to regression problems.
With this algorithm, one can now efficiently solve
large-scale regression problems (more than 20000 examples).
Comparisons with Nodelib, another publicly available
SVM algorithm for large-scale regression problems
from Flake and Lawrence (2000) yielded significant time improvements.
Finally, based on a recent paper from Lin (2000), we show that a
convergence proof exists for our algorithm.