| 摘要: |
| 在没有领域先验知识条件下的不确定知识主动式学习是机器学习领域中的一个难题.通过研究决策表和决策规则的不确定性,建立基于粗集表示、度量和处理不确定性信息和知识的理论,并且结合Skowron的缺省规则获取算法,提出一种不确定性条件下的数据自主式学习模型和方法,以解决这一问题.通过仿真实验,验证了该自主式学习方法的有效性. |
| 关键词: 不确定性 粗集 自主式学习 知识获取 机器学习 |
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| 基金项目:Supported by the National Natural Science Foundation of China under Grant No.69803014 (国家自然科学基金); the National Climb Program of the Ministry of Science and Technology of China (国家科技部攀登特别支持经费); the Foundation for University Key Teacher by the State Education Ministry of China under Grant No.GG-520-10617-1001 (高等学校骨干教师资助计划); the Scientific Research Foundation for the Returned Overseas Chinese Scholars by the State Education Ministry of China (教育部留学回国人员科研启动基金); the Application Science Foundation of Chongqing of China (重庆市应用基础研究基金); the Science and Technology Research Program of the Municipal Education Committee of Chongqing of China under Grant No.02050 (重庆市教育委员会科学技术研究项目) |
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| A Self-Learning Model under Uncertain Condition |
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WANG Guo-Yin,HE Xiao
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| Abstract: |
| It is a very difficult problem in machine learning to learn uncertain knowledge automatically without prior domain knowledge. In this paper, a theory is developed to express, measure and process uncertain information and uncertain knowledge according to uncertainty measure of decision table and decision rule. Based on the Skowron’s default rule generation algorithm, a self-learning model and the method is developed to solve this problem. Simulation results illustrate the efficiency of this self-learning method. |
| Key words: uncertainty rough set self-learning knowledge acquisition machine learning |
