The ensemble approach to neural-network learning and generalization

Igelnik B.; Yoh-Han Pao

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The ensemble approach to neural-network learning and generalization

机译：神经网络学习和综合的集成方法

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A method is suggested for learning and generalization with a general one-hidden layer feedforward neural network. This scheme encompasses the use of a linear combination of heterogeneous nodes having randomly prescribed parameter values. The learning of the parameters is realized through adaptive stochastic optimization using a generalization data set. The learning of the linear coefficients in the linear combination of nodes is achieved with a linear regression method using data from the training set. One node is learned at a time. The method allows for choosing the proper number of net nodes, and is computationally efficient. The method was tested on mathematical examples and real problems from materials science and technology.

机译：提出了一种利用通用的单层前馈神经网络进行学习和归纳的方法。该方案包括使用具有随机规定参数值的异构节点的线性组合。使用泛化数据集通过自适应随机优化来实现参数的学习。使用来自训练集的数据的线性回归方法可以实现节点线性组合中线性系数的学习。一次学习一个节点。该方法允许选择适当数量的网络节点，并且计算效率高。该方法已通过数学实例和材料科学与技术的实际问题进行了测试。

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《IEEE Transactions on Neural Networks》 |1999年第1期|P.19-30|共12页
作者
Igelnik B.; Yoh-Han Pao;
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正文语种 eng
中图分类自动化技术、计算机技术;
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The ensemble approach to neural-network learning and generalization

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