Modular Neural Network Task Decomposition Via Entropic Clustering

The use of monolithic neural networks (such as a multilayer perceptron) has some drawbacks: e.g. slow learning, weight coupling, the black box effect. These can be alleviated by the use of a modular neural network. The creation of a MNN has three steps: task decomposition, module creation and decision integration. In this paper we propose the use of an entropic clustering algorithm as a way of performing task decomposition. We present experiments on several real world classification problems that show the performance of this approach.

• Type: Conference
• Year: 2006
• Where: ISDA
• Authors: Jorge M. Santos, Luís A. Alexandre, Joaquim Marques de Sá
• Keywords: ISDA 2006, Monolithic Neural Networks, Neural Network, Operating Systems, Task Decomposition