Rule Extraction from Support Vector Machines: An Introduction
Rule Extraction from Support Vector Machines: An Introduction
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支持向量机的规则提取：简介
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发表时间：
2008
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通讯作者：
J. Diederich
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作者：
J. Diederich
从支持向量机（svm）中提取规则遵循了早期从人工神经网络（ann）中获得人类可理解规则的努力，以解释“如何”做出决策或“为什么”实现某个结果。因此，从支持向量机中提取规则领域的许多动机继承了现在建立的从神经网络中提取规则的领域。本引言旨在概述从支持向量机中提取规则的重要性，并将详细研究向可能不是人工智能或特定应用领域专家的人类用户解释机器学习系统的决策过程意味着什么。在这种情况下，同时提到心理学和哲学是很自然的，因为“解释”指的是人类的思想及其理解世界的努力；哲学研究的传统领域。因此，本文讨论了当前模拟人类解释推理的基础，以及当前从支持向量机中提取规则的限制和机会。
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.