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A Self-Generating Modular Neural Network Architecture for Supervised Learning
ABSTRACT
In this paper, we present a self-generating modular neural network
architecture for supervised learning. In the architecture, any kind of
feedforward neural networks can be employed as component nets. For a given
task, a tree-structured modular neural network is automatically generated
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 neural
network can yield both a good performance and significantly faster training.
The proposed architecture has been applied to several supervised learning
tasks and has achieved satisfactory results.
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