一种主动学习神经网络集成方法

分析了神经网络集成泛化误差、个体神经网络泛化误差、个体神经网络差异度之间的关系，提出了一种个体神经网络主动学习方法．个体神经网络同时交互训练，既满足了个体神经网络的精度要求，又满足了个体神经网络的差异性要求．另外，给出了一种个体神经网络选择性集成方法，对个体神经网络加入偏置量，增加了个体神经网络的可选数量，降低了神经网络集成的泛化误差．理论分析和实验结果表明，使用这种个体神经网络训练方法、个体神经网络选择性集成方法能够构建有效的神经网络集成系统．

An Active Learning Approach for Neural Network Ensemble

After analyzing the relationship among the generalization error of the neural networks ensemble, the generalization error, and the diversity of the individual neural network, a individual neural networks active learning algorithm ALA is proposed, which encourages individual neural network to learn from the expected goal and others, so all individual neural networks are trained simultaneously and interactively. In the stage of combining individual neural networks, a selective approach is proposed, which adds a bias to every individual network and selects part of individual networks to constitute an ensemble. Experiment results show that an efficient neural networks ensemble system can be constructed by using the individual networks learning algorithm ALA and selective ensemble approach.