Gist contains software tools for support vector machine classification and for kernel principal components analysis. The SVM portion of Gist is available via an interactive web server at http://svm.sdsc.edu. The Gist package contains the following programs:
- compute-weights trains a support vector machine based upon a given set of labeled training examples,
- classify applies a trained support vector machine to unlabeled data to produce predicted binary classifications.
- kernel-pca performs kernel principal components analysis on a given data set, and
- project projects a data set onto the components discovered by kernel-pca.
If you have problems with or questions about Gist, please first read the FAQ page.
In addition to the primary programs, the following auxiliary programs are included:
- fselect performs linear feature selection on a given data set, using binary classification labels,
- rdb-matrix performs basic manipulations of matrices,
- score-svm-results computes performance statistics from the outputs of
compute-weights
andclassify
,- gist-rfe performs SVM recursive feature elimination on a given data set,
- fit-sigmoid converts the discriminant values produced by
compute-weights
into probabilities, and- gist2html converts an output file from one of the Gist programs into HTML format.
The current version of Gist is 2.0.7.
Gist is written in ANSI C. Pre-compiled versions (including source code) are available for several platforms and can be downloaded here. Here are some installation instructions and release notes. Once you have installed the software, you can try this example.
The Gist software was written by William Stafford Noble in the Department of Computer Science at Columbia University and by Paul Pavlidis in the Columbia Genome Center.