Rule extraction from support vector machines (SVMs) follows in the footsteps of the earlier effort to obtain human-comprehensible rules from artificial neural networks (ANNs) in order to explain “how” a decision was made or “why” a certain result was achieved. Hence, much of the motivation for the field of rule extraction from support vector machines carries over from the now established area of rule extraction from neural networks. This introduction aims at outlining the significance of extracting rules from SVMs and it will investigate in detail what it means to explain the decision-making process of a machine learning system to a human user who may not be an expert on artificial intelligence or the particular application domain. It is natural to refer to both psychology and philosophy in this context because “explanation” refers to the human mind and its effort to understand the world; the traditional area of philosophical endeavours. Hence, the foundations of current efforts to simulate human explanatory reasoning are discussed as are current limitations and opportunities for rule extraction from support vector machines.
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Diederich, J. (2008). Rule Extraction from Support Vector Machines: An Introduction. In: Diederich, J. (eds) Rule Extraction from Support Vector Machines. Studies in Computational Intelligence, vol 80. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75390-2_1
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