SVMlight is an implementation of Vapnik's Support Vector Machine
[Vapnik, 1995] for the problem of pattern recognition, for the problem
of regression, and for the problem of learning a ranking function. The
optimization algorithms used in SVMlight are described in [Joachims,
2002a ]. [Joachims, 1999a]. The algorithm has scalable memory
requirements and can handle problems with many thousands of support
vectors efficiently.

The software also provides methods for assessing the generalization
performance efficiently. It includes two efficient estimation methods
for both error rate and precision/recall. XiAlpha-estimates [Joachims,
2002a, Joachims, 2000b] can be computed at essentially no
computational expense, but they are conservatively biased. Almost
unbiased estimates provides leave-one-out testing. SVMlight exploits
that the results of most leave-one-outs (often more than 99%) are
predetermined and need not be computed [Joachims, 2002a].