News

February 23rd 2017		In version 1.2 The package has been renamed to liquidSVM (formerly simons-svm). There is now a paper with many benchmarks on the arXiv. The R-package has reached version 1.0.0.
June 8th 2016		We proudly present version 1.1. Changes include: Large datasets (tested up to 10 millions of samples) can be trained and tested. A new recursive algorithm expedites the partitioning of data. Many bugs were fixed.

Packages for Different Environments

Terminal version for Linux/OS X		liquidSVM.tar.gz
Terminal version for Windows (64bit)		avx2: liquidSVM.zip avx: liquidSVM.zip sse2: liquidSVM.zip
Previous versions		v1.1 (June 2016), v1.0 (January 2016)

General Information

Installation instructions for the command line versions.

liquidSVM is licensed under AGPL 3.0. In case you need another license, please contact me.

Bindings: R

Read the demo vignette for a tutorial on installing liquidSVM-package and howto use it and the documentation vignette for more advanced installation options and usage.

Source package	liquidSVM_1.1.1.tar.gz
Binaries (SSE2)	Windows (64bit), OS X

On Linux-type systems usually a compiler is installed and hence the source package is used. On Windows and MacOS X you can use the binaries. If you want to compile the bindings from the source package, you have to install a compiler (Windows: install Rtools; on MacOS X install Xcode).

Other Bindings

The other bindings are preview versions:

	Status	Documentation
MATLAB	Tested by a few people, much better interface in preparation	README
Java	Quite stable	README and Javadoc
Python	Has not been tested much	README, demo (jupyter notebook), pydoc
Spark	Tested on a few clusters with 8-12 machines	README

Extra Datasets for the Demo

covertype data set with 35.090 training and 34.910 test samples

covertype data set with 522.909 training and 58.103 test samples

Both datasets were compiled from LIBSVM's version of the covertype dataset, which
in turn was taken from the UCI repository and preprocessed as in [RC02a].
Copyright for this dataset is by Jock A. Blackard and Colorado State University.

Citation

If you use liquidSVM, please cite it as:

I. Steinwart and P. Thomann.
liquidSVM: A fast and versatile SVM package.
ArXiv e-prints 1702.06899, February 2017.