基于可变精度粗糙集模型的有导师机器学习

机器学习是人工智能领域中重要的研究课题,基于经典粗糙集的机器学习,只有学习者的分类被完全包含在导师的分类中时,才形成决策规则,条件比较苛刻;而基于可变精度粗糙集理论的有导师机器学习,根据学习者的分类包含在导师的分类中的包含度αi,与事先给定的精度系数β的比较,来求取具有一定相容性的决策规则,该方法更具有灵活和实用性。

Supervised Machine Learning Based on Variable Precision Rough Set Theory

Machine learning is an important question for discussion in the Artificial intelligence .Based on classical rough set, and with learner’s classification completely included in the tutor ’s classification, Machine learning can form decision rule .The condition is very rigorous .Rather, Supervised machine learning based on variable precision rough set theory obtains certain compati -bility decision rules according to the comparison of the inclusion degree αi that learners ’ classification is included in the tutor ’ s classification with the given the precision coefficient β.This method is more flexible and practical .