Web Interface
The support vector machine (SVM) algorithm learns to distinguish between two given classes of data. This server 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.
This site provides access to:
- a web interface for training an SVM and classifying data,
- an overview of the SVM algorithm,
- a description of the the inputs and outputs for the SVM server, and
- source code and compiled binaries for the Gist software that powers this server.
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 in the Genome Center at Columbia University. The web server was built and is maintained by Paul Pavlidis (pp175@columbia.edu), with contributions from Ilan Wapinski, Andrew Liu and Phan Lu. The project is funded by National Science Foundation grants DBI-0078523 and ISI-0093302. Computational resources are provided by the National Biomedical Computation Resource (NBCR) at the San Diego Supercomputer Center. NBCR is funded by the National Center for Research Resources (P41 RR08605).
