混合专家聚类组合函数的评价

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. Pub Date : 2005-12-27 DOI:10.1109/IJCNN.2005.1556016

R. Redhead, M. Heywood

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引用次数: 3

摘要

专家混合模型(MoE)为构建模块化神经网络解决方案提供了基础。在这项工作中，我们感兴趣的是在转发到MoE体系结构之前分解输入的方法。通过这样做，我们能够从数据本身定义专家的数量。具体的方案被证明是适合于回归和分类问题，其中每个似乎有不同的偏好。

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Evaluation of cluster combination functions for mixture of experts

The mixtures of experts (MoE) model provides the basis for building modular neural network solutions. In this work we are interested in methods for decomposing the input before forwarding to the MoE architecture. By doing so we are able to define the number of experts from the data itself. Specific schemes are shown to be appropriate for regression and classification problems, where each appear to have different preferences.

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Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005.

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