SNIP Lab Downloads

Support Vector Machines

SVMheavy is yet another SVM library. It was originally written as a testbed to compare different incremental training methodologies, but the codebase has since been extended significantly. Both binary classification and regression are done using a unified SVM core, with incremental and decremental training abilities and parameter variation facilities built in for both. Code may be used at the command line (where some effort has been made to ensure compatibility with SVMlight), interactively, or directly from other code by accessing the SVM_pattern and SVM_regress classes. At present it supports active set training (as per "Incremental Training of Support Vector Machines", A. Shilton, M. Palaniswami, D. Ralph, A. C. Tsoi, accepted for publication in IEEE Transactions on Neural Networks), Platt's SMO algorithm and Daniel Lai's D2C algorithm. The code was written by Alistair Shilton, University of Melbourne, Electrical and Electronic Engineering department (apsh at ee.unimelb.edu.au ). Currently there is no real documentation, but we do plan to write some eventually (compilation instructions can be found in the readme file in the zip). The code has been tested in windows (compilation using DJGPP) and SunOS 5.8 (unix) and has no killer bugs that we are aware of, but use at own risk. Download SVMHeavy

Bioinformatics Downloads

Databases related to protein secondary struture prediction, real value solvent accessibility prediction, protein topology (fold) recognition, protein disulphide bridge prediction can be found on this page.