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Data Description Matlab toolbox. (version 0.9)

This toolbox is an add-on to the PRTools toolbox. The toolbox contains
algorithms to train, investigate, visualize and evaluate one-class
classifiers (or data descriptions, novelty descriptors, outlier
detectors). Some experience with the PRTools toolbox is recommended.
This toolbo is developed as a research tool so no guarantees can be
given.

- Requirements:

In order to make this toolbox work, you need:
0. A computer and some enthusiasm
1. Matlab with the - optimization toolbox (for svdd and range_svdd)
                   - statistics toolbox (for randsph)
               and - neural network toolbox (for autoenc_dd)
2. PRTools 3.0 or higher
3. This toolbox.

- Installation:

The installation of the toolbox is almost trivial. Unzip the file, store the
contents in a directory (name it for instance DD_TOOLS) and add this directory
to your matlab path.

- Information and example code:

For the most basic information, type  help DD_TOOLS (use the directory name
where the toolbox is stored). A simple one-class example is given in
dd_example.m.
Some examples of the operation of the procedures in the toolbox are given on
the web-pages:
  http://www.ph.tn.tudelft.nl/~davidt/dd_tools.html


- Notes on version 0.9:

! in the early versions of the svdd, the support vectors were
 classified as outliers. Now they are forced to be target objects.
 This will therefore change the classification results!
- added gendatout:  generation of spherically distributed outlier objects
- changed the place in which distm(a) was computed in the original
 version of svdd. In previous versions, it was done over and over
 again in f_svs, but now it is moved to the main svdd.m
- removed a bug in range_svdd, where the sqrt of the D has to be taken
 for the range of sigma.
- fixed a bug in dd_roc. Now it is possible to supply 1D datasets for
  computing the roc curve.
- fixed an error in the help of dd_auc
- added the function relabel
- replaced all explicit references of the function name by 'mfilename'
  in all one-class classifiers
- added the random_dd, which randomly assigns labels
- added lpdd.m, the linear programming data description. It works on
  distances, and therefore I also had to add:
  ddistm.m and lpdistm.m
- added kwhiten.m, normalization to unit variance in the kernel space.
  For that also center.m was needed.