Abstract：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.
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