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摘要翻译：
分类是认识的基础。与其他解决方案不同，本研究从离群点的角度来处理它。提出了一种扩展的单变量数据集异常点检测算法，并给出了算法的基础。扩展算法以整体的方式运行，使其成为一个相当稳健的解决方案。合成的和真实的数据实验显示了它的威力。此外，在多类问题的应用中引入了振子算法。相应的结果表明扩展算法具有广泛的应用前景。
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英文标题：
《On Classification from Outlier View》
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作者：
C. A. Hsiao
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最新提交年份：
2012
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分类信息：
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：
Classification is the basis of cognition. Unlike other solutions, this study approaches it from the view of outliers. We present an expanding algorithm to detect outliers in univariate datasets, together with the underlying foundation. The expanding algorithm runs in a holistic way, making it a rather robust solution. Synthetic and real data experiments show its power. Furthermore, an application for multi-class problems leads to the introduction of the oscillator algorithm. The corresponding result implies the potential wide use of the expanding algorithm.
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PDF链接：
https://arxiv.org/pdf/0907.5155
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