摘要翻译：
在许多人类理解起关键作用的领域，如生物过程，从数据中提取知识的能力至关重要。在这个框架内，如果使用得当，模糊学习方法可以极大地帮助人类专家。在这些方法中，正交变换的目的是从一组训练数据中建立规则，并通过线性回归或秩显示技术选择最重要的规则，它已经被证明是数学上鲁棒的。OLS算法是这些方法的一个很好的代表。然而，它最初被设计成只关心数值性能。因此，我们对原有的方法提出了一些修改，以考虑到可解释性。在回顾了原算法的基础上，给出了对原算法的修改，并讨论了从基准问题中得到的一些结果。最后，将该算法应用于一个实际的故障检测去污染问题。
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英文标题：
《Building an interpretable fuzzy rule base from data using Orthogonal
  Least Squares Application to a depollution problem》
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作者：
S\'ebastien Destercke (IRSN, IRIT), Serge Guillaume (ITAP), Brigitte
  Charnomordic (ASB)
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  In many fields where human understanding plays a crucial role, such as bioprocesses, the capacity of extracting knowledge from data is of critical importance. Within this framework, fuzzy learning methods, if properly used, can greatly help human experts. Amongst these methods, the aim of orthogonal transformations, which have been proven to be mathematically robust, is to build rules from a set of training data and to select the most important ones by linear regression or rank revealing techniques. The OLS algorithm is a good representative of those methods. However, it was originally designed so that it only cared about numerical performance. Thus, we propose some modifications of the original method to take interpretability into account. After recalling the original algorithm, this paper presents the changes made to the original method, then discusses some results obtained from benchmark problems. Finally, the algorithm is applied to a real-world fault detection depollution problem.
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PDF链接：
https://arxiv.org/pdf/0808.2984