a) Cristianini, N. and Shawe-Taylor, J., An introduction to Support Vector Machines :
and other kernel-based learning methods, Cambridge University Press, New York,
2000.
b) Vapnik, V. N., The nature of statistical learning theory, Springer, New York,
2000.
D2C-SVM is yet another SVM training package which implements a heuristic training
algorithm for improving the training efficiency of the SVM. This short manual is an
attempt to help the user with installing and running the Matlab GUI interface use of the
D2CSVM training package and to introduce the user to the usage of the SVM as an easy
to use pattern recognition tool.
Installation and System Requirements
D2CMatlab v 1.0 comes in a single zip file named D2CMatlab.zip which can be
downloaded from
http://www.ee.unimelb.edu.au/people/dlai/
This first version is intended to be used with the D2C-SVM classifier package and is
included in the zip file. The user needs to have Matlab v 6.01 and above installed in order
to run the GUI file. Generally, the latest computers running of Pentium IV with more
than 256 MB would have no problems in running this software. Earlier PCs shouldn’t
suffer either, just that the training of the SVM tends to become slower as your data size
increases.
Input Files
The program (v1.0) currently accepts either a training or test file in sparse format. For
example, a training example in the file will have the following format.
<label> 1: attribute 1 2: attribute 2 3: attribute 3……
e.g.
+1 1: 0.3421 3: 2.3424 5:-1.2342
- 1 4: 23.31
If your data is in matrix format, this can be easily read in using Matlab’s load function or
if the data is in an Excel file, this can be read using xlsread. The data file must then be
converted to sparse format in order to be properly used by the program.
An Excel to Spare file converter is provided in the D2CMatlab tool directory and can be
used for fast and easy conversion of data files not in sparse format.
