不完全数据下DNF的模糊化BRAIN学习算法
A fuzzified BRAIN algorithm for learning DNF from incomplete data
摘要：
本文的目的是解决从具有缺失值的训练数据中学习布尔函数的问题。我们提出了一个扩展的BRAIN算法，称为U-BRAIN（不确定性管理批量相关性为基础的人工智能），设想学习DNF布尔公式从部分真值表，可能与不确定的值或丢失的位。这种算法是从BRAIN中通过引入模糊集来管理不确定性而获得的。在不存在丢失比特的情况下，算法简化为原始BRAIN。
英文摘要：
Aim of this paper is to address the problem of learning Boolean functions from training data with missing values. We present an extension of the BRAIN algorithm, called U-BRAIN (Uncertainty-managing Batch Relevance-based Artificial INtelligence), conceived for learning DNF Boolean formulas from partial truth tables, possibly with uncertain values or missing bits. Such an algorithm is obtained from BRAIN by introducing fuzzy sets in order to manage uncertainty. In the case where no missing bits are present, the algorithm reduces to the original BRAIN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
