| 摘要: |
| 在现实世界里，ＡＩ系统难免受到噪声的影响．系统有效工作与否取决于它对噪声的敏感性如何．解释学习ＥＢＬ（ｅｘｐｌａｎａｔｉｏｎ－ｂａｓｅｄｌｅａｒｎｉｎｇ）也不例外．本文探讨了在例子受到噪声影响的情况下，解释学习的处理问题，提出了一个算法ＮＲ－ＥＢＬ（ｎｏｉｓｅ－ｒｅｓｉｓｔａｎｔＥＢＬ）．与现有的解释学习方法不同，ＮＲ－ＥＢＬ在训练例子含有噪声时仍然可以学习，以掌握实际的问题分布；和类似的工作不同，ＮＲ－ＥＢＬ指出了正确识别概念对于噪声规律的依赖性，试图从训练例子集合发现和掌握噪声的规律．可以相信，在识别概念时，借助于对噪声规律的认识，ＮＲ－ＥＢＬ可比ＥＢＬ和类似工作有更高的识别率．ＮＲ－ＥＢＬ是解释学习和统计模式识别思想的结合．它把现有的解释学习模型推广到例子含有噪声的情形，原来的ＥＢＬ算法只是它的特例． |
| 关键词: 解释学习 噪声 模式识别 |
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| EXPLANATION-BASED LEARNING ALGORITHM FOR NOISY DATA ENVIRONMENT |
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Zhang Qi,Shi Chunyi
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| Abstract: |
| In the real world, Al systems arc constantly and adversely influenced bynoisy data. This is also true of EBL (explanation-based learning). This paper discusseshow to cope with noisy data in explanation - based learning and proposes a NR -EBL(noise resistant explanation - based learning) algorithm. Unlike existing algorithms,NR-EBI. can learn macro rules and find the problem distribution when there is noise intraining examples. Also unlike similar work, NR-EBI. reveals the dependency of classifying examples correctly upon the regularities of noise and attempts to detect noise regularities from a set of training examples. With the help of knowledge of noise regularities,NR -EBI. can have a higher rate of correct recognition than traditional algorithms and previous work. NR EBI. is the combination of explanation - based learning and statisticalpattern recognition. Traditional algorithms are only special cases of NR-EBL when thereis no noise in training examples. |
| Key words: Explanation based learning noisy data pattern recognition. |
