readme

SVDD的工具箱

Data Description Matlab toolbox. (version 1.6.2)

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 toolbox 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 lpdd)
                   - statistics toolbox (for randsph)
               and - neural network toolbox (for autoenc_dd)
2. PRTools 4.0.0 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). Some simple (and some not so simple!)
one-class examples are given in dd_ex1 till dd_ex11.  For more background
information, please have a look at the pdf file included in the
directory.  Some examples of the operation of the procedures in the
toolbox are given on the web-pages:
	http://www-ict.ewi.tudelft.nl/~davidt/dd_tools.html

* Notes on version 1.6.2
- More foolproof  plotroc-version, allowing multiple plots on top of
  each other (but with just a single operating point)
- Unclear help in dd_error is fixed
- Adapted oc_set to work with the datafiles in prtools4.1
- Fixed a small scaling bug in multic.m
* Notes on version 1.6.1
- In the new Matlab (7.3) it is not allowed to do  W.w = W; Fixed it in
  multic.m
- Nicer warning messages in mogEMextend.m and mogEMupdate.m
- Better checking if things are datasets or not (using isdataset(),
  instead of using isa(,'dataset'))
- Defined the class priors in the datasets that are generated by
  dd_crossval, to avoid too many pointless warnings.
* Notes on version 1.6.0
- Significantly improved and extended the multic.m to add the
  possibility for extending the classifier with a new/unseen class. 
  Also added an example to show its use: dd_ex11.m
- Improved the auclpm by adding some more possibilities for subsampling
  constraints, and by adding the new LP optimizer
- Removed two bugs from optim_auc, and one from scale_range (Thanks to
  Anthony Brew!)
- Removed a serious bug in gendatoutg.m, where dR was not used at all!
- Remove a small checking bug in dknndd.
- Put all the input arguments in the empty mapping in gauss_dd, som_dd,
  dlpdd,
- Allow for data subsampling in myproxm.m

* Notes on version 1.5.7
- Finally added the removal of an object from the incremental SVDD. Also
  a separate function for storing the structure W to a mapping is
  introduced, and an example file (dd_ex10.m)
- Added nndist_range to easily compute the average nearest neighbor
  distance in a dataset
- Extended myproxm to include the computation of the kernel with a
  subsampled version of the training data (randomly selected prototypes)
- Tried to improve the help a bit (never ending story)
* Notes on version 1.5.6
- Added the svddpath.m and svddpath_opt.m that optimizes the SVDD by
  moving over the whole regularization path C (or lambda). It is not
  suitable for data with outliers...
- Change incsvdd such that it also outputs the distance to the sphere
  center
- Introduce variable C for different objects to weigh objects in the
  SVDD
- Greatly improved the speed of the dd_error by avoiding the call to
  renumlab.
* Notes on version 1.5.5
- Added the extended version of the lpball_dd
- Added the AUC linear optimizer auclpm.m.
- Added the rankboostm.m
* Notes on version 1.5.4
- Due to changes in prtools, I had to change make_outliers.m
- Added the possibility to incsvdd to add other Matlab files that define
  kernel functions. Furthermore, again some strange starting conditions
  have been taken care of.
- Fixed bugs in rob_gauss_dd
* Notes on version 1.5.3
- Added the equal error rate
- Updated version of mst_dd by Piotr.
* Notes on version 1.5.2
- Added the stump_dd, that thresholds just the first feature
- Small bugfixes
* Notes on version 1.5.1
- Tiny bug in oc_set for the case of selecting several classes
* Notes on version 1.5.0
- Changed the way in which the warnings are made: use the Matlab warning
  identifiers
- Added the cost curve, a derivative curve from the ROC curve. The curve
  is created using dd_costc, and can be plotted using  plotcostc.m.
- The functionality of plotroc and plotcosts is extended.
- Take care for zero-variance features in ball_dd.
- Remove the C-variable from ball_dd, because it was (almost) pointless
- Added the multic, that combines one-class classifiers into one
  multi-class classifier
- Removed a sneaky bug in oc_set; oc_set(x,'outlier') works now
  correctly when one-class dataset x does not contain outliers
- Made the simpleroc.m a bit more beautiful and consistent.

* Notes on version 1.4.1
- Removed a *stupid* typo in the definition of an empty mapping

* Notes on version 1.4.0
- Changed the storage of the classifiers that are using normal
  distributions. Instead of the covariance matrix, the inverse of the
  covariance matrix is stored, making the repeated inverse computation
  in the application of the mapping unnecessary
- Added the Minimum spanning tree data description by Piotr Juszczak and
  a plotting function to show the tree on screen.
- Made an improved EM procedure for training the mixture of Gaussians.
  It is not only possible to train with example outliers, it is now also
  possible to extend the number of clusters for a trained mixture. Here
  also the inverse covariance matrix is stored. It is also possible to
  change the threshold for a trained mapping.
- In the function dd_roc the special case that two (or more) objects are
  on exactly the same place, is covered
- New plot (without a good name, so I called it askerplot for historical
  reasons)
- Fixed the wrong output threshold vector that was provided by
  simpleroc.m
- Fixed a bug in dd_setfn.m, the threshold should be fitted on the target
  data, not on all the data
- Small bug fixes and minor features added (for instance, output the
  mean and radius from the function gendatout.m, or return the optimal
  parameter in consistent_occ)

* Notes on version 1.3.0
- Introduced  optim_auc  to automatically update hyperparameters by
  optimizing AUC
- Added the Naive Bayes data description
- Added the Minimax probability machine data description.
- Added a L_p Ball data description
- Changed the simpleroc, such that objects with identical output values
  do not cause the ROC to change when the dataset is randomized in
  ordering. It still results in an suspicious ROC curve, but I added a
  warning
- Made the change_R more robust against overflow (thanks to Mauro Del
  Rio)
- removed a bug in som_dd, concerning the input arguments that were not
  set correctly
- removed a bug from mykmeans, where the average over one object should
  be avoided, grrrr
- removed a bug from consistent_occ, where the loop over k was not
  stopped on time
- minor tweakings concerning speed, pointless (and not so pointless)
  warnings, help and tiny bugs

* Notes on version 1.2.0
- Removed a stupid typo in ksvdd
- Made the function setthres.m actually useful (after quite some
  requests), replaced by  dd_setfn.m
- made an extra check in isocc
- Made a significant change and improvement in the plotroc function. It
  is now possible to dynamically change the operating point of a
  one-class classifier.