The support vector machine (SVM) algorithm learns to distinguish between two given classes of data. This page allows you to train an SVM on a labeled training set and then use the trained SVM to make predictions about the classifications of an unlabeled test set.
We appreciate suggestions or bug reports.
There is more documentation on file formats and the SVM algorithm plus a FAQ page.
Please note there are some limitations to how you can use this site. Very large or long-running jobs cannot be run. The number of concurrent or waiting jobs is limited. If you have large data sets, or want to run the SVM many times, try the command line tools. The command line tools also give you access to additional features of the software such as feature selection and built-in cross-validation.
Send reports of problems to firstname.lastname@example.org
The svm software was developed by William Stafford Noble in the Department of Genome Sciences and Computer Science at the University of Washington and Paul Pavlidis (University of British Columbia). The web server was built and is maintained by Paul Pavlidis (paul\@chibi.ubc.ca), with contributions from Ilan Wapinski, Andrew Liu and Phan Lu. The project was funded by National Science Foundation grants DBI-0078523 and ISI-0093302.