Abstract
Rule extraction is an important method seeking to understand how neural networks are able to solve problems. In order for rule extraction to be comprehensible, good knowledge representations should be used. So called M of N rules are a compact way of representing knowledge that has a strong intuitive connection to the structure of neural networks. M of N rules have been used in the past in the context of supervised models but not unsupervised models. Here we present a novel extension of a previous rule extraction algorithm for RBMs that allows us to quickly extract accurate M of N rules. The results are compared on simple datasets showing that M of N extraction has the potential to be an effective method for the knowledge representation of RBMs.
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Odense, S., d’Avila Garcez, A. (2017). Extracting M of N Rules from Restricted Boltzmann Machines. In: Lintas, A., Rovetta, S., Verschure, P., Villa, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2017. ICANN 2017. Lecture Notes in Computer Science(), vol 10614. Springer, Cham. https://doi.org/10.1007/978-3-319-68612-7_14
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DOI: https://doi.org/10.1007/978-3-319-68612-7_14
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