配对属性学习:利用配对特征构建网络

Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290) Pub Date : 2002-08-07 DOI:10.1109/IJCNN.2002.1007546

Tony R. Martinez, martinez

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引用次数: 1

摘要

我们提出了对属性学习(PAL)算法来选择相关输入和网络拓扑。利用训练实例对的相关性驱动单隐层MLP的网络构建。9个学习问题的结果显示，平均而言，复杂性降低了70%，而准确性没有显著下降。

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Pair attribute learning: network construction using pair features

We present the pair attribute learning (PAL) algorithm for the selection of relevant inputs and network topology. Correlations on training instance pairs are used to drive network construction of a single-hidden layer MLP. Results on nine learning problems demonstrate 70% less complexity, on average, without a significant loss of accuracy.

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Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290)

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