This paper outlines some key software components developed in the Pattern Recognition Engineering (PaREn) project. The goal of the PaREn project was to create the methods and tools necessary allowing non-experts to use, train, test, and deploy pattern recognition and machine learning modules in real-world software systems. A major effort in the PaREn project was therefore automating parameter optimization, model selection, machine learning system construction, and supporting rapid testing, validation, and on-line adaptivity. To deliver our technologies as open source, we chose RapidMiner as the software platform. Therefore, major software components developed in PaREn are provided as RapidMiner extensions. The expected benefits are a far wider usage of pattern recognition and machine learning methods, leading to both better quality of the decisions and behaviors of software systems, as well as lower development costs.
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