The dd_tools Matlab toolbox provides tools, classifiers and evaluation functions for the research of one-class classification (or data description). The dd_tools toolbox is an extension of the Prtools toolbox in which Matlab objects for mapping and dataset are defined. dd_tools uses these objects and their methods, but extends (and sometimes restricts) them to one-class classification. This means that before you can use dd_tools to its full potential, you need to know a bit about Prtools. When you are completely new to pattern recognition, Matlab or Prtools, please familiarize yourself a bit with them first (see http://www.prtools.org for more information on Prtools).
This short document should give the reader some idea what the data description toolbox (dd_tools) for Prtools offers. It provides some background information about one-class classification, about some implementation issues and it gives some practical examples. It does not try to be complete, though, because each new version of the dd_tools will probably include new commands and possibilities. The file Contents.m in the dd_tools-directory gives the up-to-date list of all functions and classifiers in the toolbox. The most up-to-date information can be found on the webpage on dd_tools, currently at: http://www-ict.ewi.tudelft.nl/~davidt/dd_tools.html
Note, that this is not a cookbook, solving all your problems. It
should point out the basic philosophy of the dd_tools .
You should always have a look at the help provided by each command (try
help dd_tools). They should show all possible combinations of
parameter arguments and output arguments. When a parameter is listed in
the Matlab code, but not in the help, it often indicates an undocumented
feature, which means: be careful! Then I'm not 
sure if it will
work, how useful it is and if it will survive a next dd_tools version.
In chapter 2 a basic introduction about one-class classification/novelty detection/outlier detection is given. What is the goal, and how is the performance measured. You can skip that if you're familiar with one-class classification. In chapter 2.4 the basic idea of the dd_tools is given. Then in chapters 3 and 4 the specific use of datasets and classifiers is shown. In chapter 5 the computation of the error is explained, and finally in 6 some general remarks are given.