The software requires the Java runtime environment >=1.5.0 (JRE 5.0) which can be downloaded from oracle.
VennMaster is packed into a single zip archive which must be unzipped into the desired directory (e.g. "C:\Program Files\" or your home directory). Call venn.sh in the VennMaster directory from the command line (Linux or Mac OS X) or double click venn.bat under Microsoft Windows in the file explorer.
When using many categories VennMaster may report an out of memory error. In these cases reduce the number of categories by specifying other import filter settings or open venn.sh (Linux or Mac OS X) or venn.bat (Windows) with a text editor (e.g. vi or notepad) and change the memory size by editing the two -X options of the java call. The -X options specify the memory size (in MB) assigned to the software. These can be adapted depending on the available memory and the problem size (number of categories/elements). The standard settings are usually too small for the most problems (2MB for Sun Java 1.5 on linux and 1MBon win).
VennMaster_0.38.0.zip (Version 0.37.5)
VennMaster-0.37.5.zip (Version 0.37.5)
VennMaster-0.37.4.zip (Version 0.37.4)
VennMaster-0.37.3.zip (Version 0.37.3)
VennMaster-src-0.37.3-20080219.zip (Source code version 0.37.3)
VennMaster-0.37.2.zip (Version 0.37.2)
VennMaster-src-0.37.2-20080110.zip (Source code version 0.37.2)
VennMaster-0.37.0.zip (Version 0.37.0)
VennMaster-src-0.37.0-20071119.zip (Sour ce code version 0.37.0)
VennMaster-0.36.0.zip (Version 0.36.0)
VennMaster-src-0.36.0-20070907.zip (Source code version 0.36.0)
VennMaster-0.35.0.zip (Version 0.35.0)
VennMaster-src-0.35.0-20070806.zip (Source code version 0.35.0)
VennMaster sources are maintained at GitHub. Visit the groups development page at https://github.com/sysbio-ulm/
VennMaster and the VennMaster source code are licensed under a
This work is licensed under a Creative Commons Attribution-Noncommercial 3.0 License.
Our paper "Constraining classifiers in molecular analysis: invariance and robustness" has been published in Journal of the Royal Society Interface.
Our paper "Two-Stream Attention Network for Pain Recognition from Video Sequences" has been published in Sensors (Basel).