Dpto. Informática de Sistemas, Universidad Católica San Antonio, Murcia, Spain.
Neural Netw. 2013 Dec;48:19-24. doi: 10.1016/j.neunet.2013.06.010. Epub 2013 Jul 2.
Selection of the optimal neural architecture to solve a pattern classification problem entails to choose the relevant input units, the number of hidden neurons and its corresponding interconnection weights. This problem has been widely studied in many research works but their solutions usually involve excessive computational cost in most of the problems and they do not provide a unique solution. This paper proposes a new technique to efficiently design the MultiLayer Perceptron (MLP) architecture for classification using the Extreme Learning Machine (ELM) algorithm. The proposed method provides a high generalization capability and a unique solution for the architecture design. Moreover, the selected final network only retains those input connections that are relevant for the classification task. Experimental results show these advantages.
选择最佳的神经网络结构来解决模式分类问题需要选择相关的输入单元、隐藏神经元的数量及其相应的互连权重。这个问题已经在许多研究工作中得到了广泛的研究，但是它们的解决方案通常涉及到大多数问题中的过度计算成本，并且它们不提供唯一的解决方案。本文提出了一种新的技术，使用极限学习机（ELM）算法来有效地设计用于分类的多层感知器（MLP）架构。所提出的方法为体系结构设计提供了高度的泛化能力和唯一的解决方案。此外，所选的最终网络仅保留那些与分类任务相关的输入连接。实验结果表明了这些优势。
