The NNHEP Project
NNHEP is a neural network package adopted for problems in HEP.
The main idea of the project is to prepare a software framework, where people can
easily add new components (modules, plugins) with new learning algorithms, network
architectures, etc. The software design should allow all the elements are properly
interfaced with each other in order to create a chain of the desired methods for
analysis. In this way, it will be possible to combine different algorithms in the
analysis chain. For example, in the pre-processing of input data one can use
unsupervised learning methods, like Self Organizing Map (SOM), and then pass the SOM
output to the feed-forward neural network (NN), use Genetic Algorithms (GA) to optimise
the parameters of NN, add a boosting algorithm in order to improve the efficiency and
plot necessary monitoring/final distributions with the modern level of graphics (e.g.
in ROOT package). In the same way user should have a possibility to change the
intermediate components (e.g. use Bayes NN (BNN) instead of NN) and implement new
training methods, etc. It is written in C++.
Any questions or comments should be directed to firstname.lastname@example.org.