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特征空间是人工智能领域中经常使用的基本概念之一．人工智能领域中的许多问题都可以通过特征空间变换的方法化简和求解．文中提出了一种基于示例学习的特征空间变换方法．该方法可以应用到智能控制、模式识别、知识获取等方面．给出的数字实验结果表明，基于示例学习的特征空间变换方法不仅具有重要的理论意义，而且具有显著的实际效果．文中还给出了通过改变描述输出空间，提高学习效率的方法．结果表明该方法也是非常有效的．
Feature space is one of the basic concepts often used in the field of artificial intelligence. Many problems in the field of artificial intelligence can be simplified and solved through the method of feature space transformation. In this paper, a method of feature space transformation based on example learning is proposed. The method can be applied to intelligent control, pattern recognition, knowledge acquisition and so on. The experimental results show that the method of feature space transformation based on sample learning not only has important theoretical significance, but also has significant practical effects. The article also gives a method to improve learning efficiency by changing the description of output space. The results show that the method is also very effective.