SVMLight is an implementation of a Support Vector Machine - supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis.
The main features of the program are the the following:
- fast optimization algorithm
- working set selection based on steepest feasible descent
- "shrinking" heuristic
- caching of kernel evaluations
- use of folding in the linear case
- solves classification and regression problems. For multivariate and structured outputs use
SVMstruct. - solves ranking problems (e. g. learning retrieval functions in STRIVER search engine).
- computes
XiAlpha-estimatesof the error rate, the precision, and the recall - efficiently computes
Leave-One-Outestimates of the error rate, the precision, and the recall - includes algorithm for approximately training large transductive SVMs (TSVMs) (see also Spectral Graph Transducer)
- can train SVMs with cost models and example dependent costs
- allows restarts from specified vector of dual variables
- handles many thousands of support vectors
- handles several hundred-thousands of training examples
- supports standard kernel functions and lets you define your own
- uses sparse vector representation
