Learning Verifiable Ensembles for Classification Problems with High Safety Requirements

Machine learning methods are successfully applied in a wide range of applications – for instance – object recognition in computer vision, search engines, or stock market analysis. But in the field of safetyrelated applications such methods are regarded with suspicion by the domain experts because the learned models are often hard to verify, may tend to overfitting, and the exact interand extrapolation behavior is often unclear. In this chapter, a machine learning method is proposed that (1) is capable of ABSTRACT

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