medSTC-package: Max-margin supervised Sparse Topical Coding model (Med-STC)

In medSTC: A max-margin supervised Sparse Topical Coding Model

Description

This package employs sparse topical coding models for multi-class classification developed by Jun Zhu and Eric P. Xing. The package uses a fast C implementation of SVM, SVMlight (http://svmlight.joachims.org/), developed by Thorsten Joachims <[e-mail]>.

Details

Package:	medSTC
Type:	Package
Version:	1.0.0
Date:	2013-1-15
License:	GPL
LazyLoad:	yes

Author(s)

Jun Zhu ([e-mail]), Aykut Firat ([e-mail])

References

Jun Zhu, and Eric P. Xing. Sparse Topical Coding, In Proc. of 27th Conference on Uncertainty in Artificial Intelligence (UAI), Barcelona, Spain, 2011. T. Joachims, Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning, B. Scholkopf and C. Burges and A. Smola (ed.), MIT-Press, 1999. http://www-ai.cs.uni-dortmund.de/DOKUMENTE/joachims_99a.pdf

See Also

Functions to fit models: medSTC

Functions for prediction: predict.medSTC

Included data sets: newsgroups

Examples

## See the demo for the newsgroup example:
## Not run: demo(medSTC)

Example output

medSTC documentation built on May 29, 2017, 5:13 p.m.