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 ). 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


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) and caleckie (at) 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.

Internet of Things Data: A pilot deployment in the city of Melbourne, Australia

As part of the 'Internet of Things (IoT) for Creating Smart Cities' project, the ISSNIP group of the University of Melbourne, ARUP and the City of Melbourne council (CoM) have done a pilot deployment of IoT networks, in the Melbourne city, for monitoring environmental parameters. One of the aims of the research is to develop new systems and algorithms that can help City administrators remotely monitor, understand and interpret real time information on urban environments. The environmental sensors, measuring light levels, humidity and temperature, have been deployed at Fitzroy GardensandLibrary at the Dock. The data collected will assist the Urban Landscapes branch of CoM to better understand and communicate the impact of canopy cover for urban cooling.

Real time Data:
There are five sensors deployed at the Fitzroy Garden and four sensors deployed at the Docklands library. The data is collected at 10 minutes interval and the live data is available from the open data platform of CoM. The data is being collected since 15th December 2014. The link is provided below:

This data may be used provided you acknowledge its use by including the following two citation [1,2].

[1] Alistair Shilton, Sutharshan Rajasegarar, Christopher Leckie, Marimuthu Palaniswami " DP1SVM: A Dynamic Planar One-Class Support Vector Machine for Internet of Things Environment", In proceedings of the International Conference on Recent advances in Internet of Things (RIoT 2015), April, 2015.

[2] Jayavardhana Gubbi, Rajkumar Buyya, Slaven Marusic, and Marimuthu Palaniswami, Internet of Things (IoT): A Vision, Architectural Elements, and Future Directions, Future Generation Computer Systems, Volume 29, No. 7, Pages: 1645-1660, ISSN: 0167-739X, Elsevier Science, Amsterdam, The Netherlands, 2013.

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.

More information about this deployment can be seen from the following link:

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