Dr Joerg Stelzer (DESY, Germany)
At the dawn of LHC data taking, multivariate data analysis techniques have become the core of many physics analyses. TMVA provides easy access to sophisticated multivariate classifiers and is widely used to study and deploy these for data selection. Beyond classification, most multivariate methods in TMVA perform regression optimization which can be used to predict data corrections, e.g. for calibration or shower corrections. The tightening of the integration with ROOT provides a common platform for discussion between the user community and the TMVA devolopers. The talk gives an overview of the new features in TMVA such as regression, multi-class classification and cathegorization, the extented pre-processing capabilities, and planned further developments.