Adaptively fusing neural network predictors toward higher accuracy: A case study

Yunfeng Wu, S. Ng
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引用次数: 1

Abstract

In order to provide function approximation solutions with high accuracy, we employ a multi-learner system that combines a group of component neural networks (CNNs) with an adaptive weighted fusion (AWF) method. In the AWF, the optimization of the normalized weights is obtained with the constrained quadratic programming. Depending on the prediction errors of the CNNs from one input sample to another, the AWF can adaptively adjust the weights which are assigned to the CNNs. The results of the function approximation experiments on six benchmark data sets demonstrate that the AWF method can effectively help the multi-learner system achieve higher accuracy (measured in terms of mean-squared error) of prediction, in comparison with the popular the Bagging algorithm.
面向更高精度的自适应融合神经网络预测器:一个案例研究
为了提供高精度的函数逼近解,我们采用了一种多学习器系统,该系统将一组组件神经网络(cnn)与自适应加权融合(AWF)方法相结合。在AWF中,利用约束二次规划方法对归一化权值进行优化。AWF可以根据cnn从一个输入样本到另一个输入样本的预测误差,自适应调整分配给cnn的权值。在6个基准数据集上的函数逼近实验结果表明,与目前流行的Bagging算法相比,AWF方法可以有效地帮助多学习器系统实现更高的预测精度(以均方误差衡量)。
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