自组织决策树的联想记忆在线学习模型

Online Associative Memory Model Based on Self-organizing Decision Tree

针对多源数据在线学习环境下的联想记忆建模问题,并综合考虑计算高效性、噪声鲁棒性等目标,提出基于自组织决策树的联想记忆在线学习模型.首先根据模式数据内在结构进行类内信息增强和噪声约简,然后基于信息熵增益的决策树算法对约简后数据进行子域划分,最后通过子域关系学习建模多源数据的联想关系.理论分析模型的学习稳定性.实验表明,文中模型在含噪数据在线分类学习和异联想建模问题上具有优良性能.

To model associative relationships among multiple-source data in online way, an online associative memory model based on self-organizing decision tree is proposed with the consideration of the efficient computation performance and good noise robustness. In the proposed model, real multi-source data are firstly reduced into finite representatives for information enhancement. Then, data representatives are divided into different sub-domains based on decision tree algorithm. Finally, the associative relations among multi-source data are trained on different sub-domains. The learning stability of the proposed model is analyzed theoretically. The experimental results demonstrate the proposed model can gain good performance on online classification learning and hetero-associative modeling for noisy data.