1) Fisher and LD
-----------------
"Bugfix" in Fisher's linear discriminant:
 The "Within-Class" matrix was before calculated as: 
       
  (*fWith)(x, y) = (sumSig[k] + sumBgd[k])/(fSumOfWeightsS + fSumOfWeightsB);
 while according to Fisher it should (and now is) be:
  (*fWith)(x, y) = sumSig[k]/fSumOfWeightsS + sumBgd[k]/fSumOfWeightsB;    

Both results are the same if the number of (weighted) training events for signal
and background are the same, and as this is the recommended setting, the "bug" 
had very litte impact. However in order to be a "correct" Fisher discriminant, 
the correct calculation has now been adopted. Fisher and LD are the same, ONCE
the events are weighted such that signal and background have the same weight. 
Hence, the LD classifier still gives exactly the same result as the "old" Fisher
implementation, while the corrected Fisher implementation allows to "play" with
different event weights to perhaps find better discrimination power in certain
regions of the ROC curve.

 
2) BDT

 a) Changes to some tuning options 

     nEventsMin          --> MinNodeSize
     UseNTrainEvents     --> BaggedSampleFraction

   have been replaced by options that are now given in terms of the relative
   size of the training sample rather than in absulut numbers of events. This
   is in order to facilitate the parameter tuning on different sample sizes
   (i.e when starting on a smaller data sample to tune the parameter in order
    to speed up the training)
    Furthermore, this option here has been changed name

     GradBaggingFraction --> BaggedSampleFraction

    in an attempt to consolidate  and avoid idential duplicate code
   
    The option UseWeightedTrees has been removed and set to "true", as was default
    anyway, as a measure of further consolidation

    Removed the option NNodesMax --> This should be replaced by specifying MaxDepth
     instead (limiting the maximum tree depth also limits the number of possible nodes!)

 b) Added a trial version of a new "cost sensitive" boosting algorithem according to
    Wei Fan and Salvatore J. Stolfo, {\em AdaCost: misclassification cost-sensitive boosting}, Proceedings of the 16th International conference on machine learning (ICML 1999)}. With the currently
chosen DEFAULT settings (all costs equal and set to "one"), it is equivalent to the "real-AdaBoost" (i.e. using the option !UseYesNoLeaf (which uses the leave node purity rather than a signal or background attribute in the leaf node of each individual tree). Unfortunatly, no reasonable performance has been achieved yet when choosing different cost parameters.

 c) BDT's with little tree depth (as favoured for good performance) do not *like* it if
    there are very clean signal and background separation cuts available, which however 
    have NOT been applied yet as preselection. Now there is a possibility to choose the option
    "DoPreselection" that looks for suitable preselection cuts and applies them prior to 
    the Decision Tree training. While that works fine, this clearly gives "sharp" peaks at +1 (-1)
    for the MVA output distribution and therefore the "smoothing" of this distribution used to
    produce the ROC curve and efficiency estimates are somewhat thwarted.
   
    --> It's better if you do these preselection cuts YOURSELF when defining training and test
    sample!

 d) Removed completely the (hopefully never used) option of treating negative events weights
    via: PairNegWeightsInNode

 e) Renamed option: IgnoreNegEvents --> IgnoreNegEventsInTraining
    and removed the IDENTICAL option NoNegeventsInTraining

6) SVM
   All but the Gauss kernel options have been "removed" (guess that was done already some
   time ago, probably with the introduction of "regression", but was not properly announced
   so far

5) minor bug fixes 

   a) fix calculation of mean values of the MVA distribution
      for signal and background samples, which is used to
      decide if a cut on the MVA variable selects signal or
      background. Due to the bug it sometimes was swapped.

   b) equalize the interpolation of the PDF-class that is used to
      smooth the Gauss-Transformation, between the .xml weights and
      the Standalone class. Now they give the same results even for
      large data samples where numerical difference previously 
      resulted in substantial differences.