【摘要】 提出一种新的信度网结构在线学习算法.其核心思想是,利用新样本对信度网结构和参数不断进行增量式修改,以逐步逼近真实模型.本算法分为两个步骤:首先分别利用参数增量修改律和添加边、删除边、边反向3种结构增量修改律,并结合新采集的样本,对当前信度网模型进行增量式修改;然后利用结果选择判定准则。从增量式修改所得的后代信度网集合中选择一个合适的信度网作为本次迭代结果.该结果在与当前样本的一致性和与上一代模型的距离之间达到一个合理的折衷.实验结果表明,本算法能有效地实现信度网结构的在线学习.由于在线学习不需要历史样本,且能够不断适应问题域的变化,适合于对具有时变性的领域进行信度网建模.更多还原

【Abstract】 An on-line structure-learning algorithm of belief network is proposed. The basic idea is to incrementally update the structure and parameters of a belief network after each group of data samples is received. The algorithm consists of two steps. The first step is to update the current belief network based on newly received data samples using incremental updating rules, including parameter incremental updating rule and three structure incremental updating rules, which are adding edge, deleting edge and reverting edge. The second step is to use the result selection criterion to select the most appropriate result from the set of candidates resulted by the first step. The selection criterion fulfills the desire to balance the consistency of the result with the newly received data against the distance between the result and the previous model. Experimental results show that the algorithm can efficiently perform on-line learning of belief network structure. Since on-line learning does not need history data and can adapt to the variation of the problem domain, this algorithm is suitable to model those domains that vary with time.更多还原