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The Stony Brook Algorithm Repository

Steven Skiena
Stony Brook University
Dept. of Computer Science

SVMlight implementation of Support Vector Machines

SVMlight is an implementation of Support Vector Machines (SVMs) in C. The main features of the program are the following:

* fast optimization algorithm o working set selection based on steepest feasible descent o "shrinking" heuristic o caching of kernel evaluations o 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-estimates of the error rate, the precision, and the recall * efficiently computes Leave-One-Out estimates 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

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    This page last modified on 2008-07-10 .