NEURAL-NETWORK STUDIES .1. COMPARISON OF OVERFITTING AND OVERTRAINING
NEURAL-NETWORK STUDIES .1. COMPARISON OF OVERFITTING AND OVERTRAINING
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DOI：
10.1021/ci00027a006
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发表时间：
1995-09-01
期刊：
影响因子：
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通讯作者：
LUIK, AI
中科院分区：
文献类型：
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作者：
TETKO, IV;LIVINGSTONE, DJ;LUIK, AI
本文用人工结构化数据集和真实的文献数据，研究了前馈-反向传播人工神经网络（ANN）在多元线性回归（MLR）等价中的应用。网络的预测能力已经估计使用训练/测试集协议。结果表明，人工神经网络的优点MLR分析。人工神经网络不需要高阶项或指标变量来建立复杂的结构-活性关系。通过交叉验证避免过训练，过拟合对网络预测能力没有任何影响。应用人工神经网络集成允许避免的机会相关性和令人满意的预测的新数据已经获得了广泛的数量的神经元在隐藏层。
The application of feed forward back propagation artificial neural networks with one hidden layer (ANN) to perform the equivalent of multiple linear regression (MLR) has been examined using artificial structured data sets and real literature data. The predictive ability of the networks has been estimated using a training/test set protocol. The results have shown advantages of ANN over MLR analysis. The ANNs do not require high order terms or indicator variables to establish complex structure-activity relationships. Overfitting does not have any influence on network prediction ability when overtraining is avoided by cross-validation. Application of ANN ensembles has allowed the avoidance of chance correlations and satisfactory predictions of new data have been obtained for a wide range of numbers of neurons in the hidden layer.