关键词: 模块神经网络, 自构筑, 监督学习

Abstract: Presented a novel self-architecure modular neural network architecture, called modular-tree for supervised learning. In the architecture, any kind of feedforward neural networks can be employed as componenets and a modular neural network with the tree structure is generated automatically with a growing algorithm by partitioning input space recursively to avoid the problem of pre-determined structure. Due to the principle of divide-and-conquer used in the proposed architecture, the modular-tree can yield both a good performance and significantly fast training. The proposed architecture has been applied to several supervised learning tasks including both benchmark and real-world problems and achieved satisfactory results.

Key words: modular neural networks, self-architecture, supervised learning