Updated README

README.md

@@ -1,12 +1,12 @@
 # FastBDT
 
-Stochastic Gradient Boosted Decision Trees, usable standalone, as TMVA Plugin and via Python Interface.
+Stochastic Gradient Boosted Decision Trees, usable standalone, and via Python Interface.
 
 # Paper on ArXiv: http://arxiv.org/abs/1609.06119
 
 FastBDT: A speed-optimized and cache-friendly implementation of stochastic gradient-boosted decision trees for multivariate classification
 
-Stochastic gradient-boosted decision trees are widely employed for multivariate classification and regression tasks. This paper presents a speed-optimized and cache-friendly implementation for multivariate classification called FastBDT. FastBDT is one order of magnitude faster during the fitting-phase and application-phase, in comparison with popular implementations in software frameworks like TMVA, scikit-learn and XGBoost. The concepts used to optimize the execution time and performance studies are discussed in detail in this paper. The key ideas include: An equal-frequency binning on the input data, which allows replacing expensive floating-point with integer operations, while at the same time increasing the quality of the classification; a cache-friendly linear access pattern to the input data, in contrast to usual implementations, which exhibit a random access pattern. FastBDT provides interfaces to C/C++, Python and TMVA. It is extensively used in the field of high energy physics by the Belle II experiment. 
+Stochastic gradient-boosted decision trees are widely employed for multivariate classification and regression tasks. This paper presents a speed-optimized and cache-friendly implementation for multivariate classification called FastBDT. FastBDT is one order of magnitude faster during the fitting-phase and application-phase, in comparison with popular implementations in software frameworks like TMVA, scikit-learn and XGBoost. The concepts used to optimize the execution time and performance studies are discussed in detail in this paper. The key ideas include: An equal-frequency binning on the input data, which allows replacing expensive floating-point with integer operations, while at the same time increasing the quality of the classification; a cache-friendly linear access pattern to the input data, in contrast to usual implementations, which exhibit a random access pattern. FastBDT provides interfaces to C/C++ and Python. It is extensively used in the field of high energy physics by the Belle II experiment. 
 
 
 # Installation
@@ -15,26 +15,18 @@ Stochastic gradient-boosted decision trees are widely employed for multivariate
   * make
   * make install
   * make package (optional to build rpm, deb packages)
+  * python3 setup.py install (optional to install the python package)
+
 
 # Usage
 
 Before you do anything you want to execute the unittests:
   * ./unittest
 
-
-There are multiple ways to use FastBDT. 
-I prepared some standalone executables
-  * ./FastBDTMain 
-  * or python3 tools/FastBDTMain.py
-
-for the python3 version you probably have to set the LD_LIBRARY_PATH
-
-
 But usually it should be more convinient to use FastBDT as a library
 and integrate FastBDT directly into your application using
   * the C++ shared/static library (see example/CPPExample.cxx),
   * the C shared library,
-  * the TMVA plugin if you're using ROOT (see root examples/TMVAExample.C ),
   * or the Python3 library python/FastBDT.py (see example/PythonExample.py ).
 
 
@@ -43,3 +35,6 @@ This work is mostly based on the papers by Jerome H. Friedman
   * https://statweb.stanford.edu/~jhf/ftp/trebst.pdf
   * https://statweb.stanford.edu/~jhf/ftp/stobst.pdf
 
+FastBDT also implements the uGB techniques to boost to flatness:
+  * https://arxiv.org/abs/1410.4140
+