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
Databases related to protein secondary struture prediction, real value solvent accessibility prediction, protein topology (fold) recognition, protein disulphide bridge prediction can be downloaded from here.
Labelled Wireless Sensor Network Data Repository (LWSNDR)
This page provides a labelled wireless sensor network data set collected from a simple single-hop and a multi-hop wireless sensor network deployment using TelosB motes. The data consists of humidity and temperature measurements collected during 6 hour period at intervals of 5 seconds. Single-hop data is collected on 9th May 2010, and the multi-hop data is collected on 10th July 2010. Label ‘0’ denotes normal data and label ‘1’ denotes an introduced event. In this case steam from hot water is introduced to increase the humidity and temperature. More details can be obtained from [1].
Download – SingleHopLabelledReadings
Download – MultiHopLebelledReadings
This data may be used provided you acknowledge its use by including the following citation [1].
We are interested to hear from people who have used this data. Please email ssuthaharan (at) uncg.edu and caleckie (at) unimelb.edu.au if you have used this dataset, or have an additional data set you would like to contribute.
[1] Shan Suthaharan, Mohammed Alzahrani, Sutharshan Rajasegarar, Christopher Leckie and Marimuthu Palaniswami, "Labelled Data Collection for Anomaly Detection in Wireless Sensor Networks", in Proceedings of the Sixth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2010), Brisbane, Australia, Dec 2010.
Permission is granted to use this data in any format, and we make no warranties as to the quality or accuracy of any of the data linked from this web page.
