首页> 外文会议>IAPR Workshop on Artificial Neural Networks in Pattern Recognition(ANNPR 2006); 20060831-0902; Ulm(DE) >Combining MF Networks: A Comparison Among Statistical Methods and Stacked Generalization
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Combining MF Networks: A Comparison Among Statistical Methods and Stacked Generalization

机译:结合MF网络:统计方法和堆叠概括之间的比较

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The two key factors to design an ensemble of neural networks are how to train the individual networks and how to combine the different outputs to get a single output. In this paper we focus on the combination module. We have proposed two methods based on Stacked Generalization as the combination module of an ensemble of neural networks. In this paper we have performed a comparison among the two versions of Stacked Generalization and six statistical combination methods in order to get the best combination method. We have used the mean increase of performance and the mean percentage or error reduction for the comparison. The results show that the methods based on Stacked Generalization are better than classical combiners.
机译:设计神经网络集成的两个关键因素是如何训练单个网络以及如何组合不同的输出以获得单个输出。在本文中,我们重点介绍组合模块。我们提出了两种基于堆叠泛化的方法作为神经网络集成的组合模块。在本文中,我们对两种版本的Stacked Generalization和6种统计组合方法进行了比较,以获得最佳组合方法。在比较中,我们使用了平均性能提升和平均百分比或误差减少。结果表明,基于堆叠泛化的方法优于经典组合器。

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