神经网络集成

神经网络集成通过训练多个神经网络并将成结论进行合成，可以显著地提高学习系统的泛化能力。它不仅有助于科学家对机器学习和神经的深入研究，还有助于普通工程技术人员利用神经网络技术来解决真实世界中的问题。因此，它被视为一种广阔应用前景的工程化神经计算技术，已经成为机器学习和神经计算领域的研究热点。该文从实现方法、理论分析和应用成果等三个方面综述了神经网络集成的国际研究现状，并对该领域值得进一步研究的一些问题进行了讨论。

Neural Network Ensemble

Neural network ensemble can significantly improve the generalization ability of learning systems through training a finite number of neural networks and then combining their results. It is not only helpful for scientists to investigate machine learning and neural computing but also helpful for common engineers to solve real world problems using neural network techniques. Therefore neural network ensemble has been regarded as an engineering neural computing technology that has great application prospect. Also it has become a hot topic in both machine learning and neural computing communities. In this paper, the state of the art of neural network ensemble is surveyed from three aspects including implementation methods, theoretical analysis, and applications. Moreover, some issues valuable for future exploration in this area are indicated and discussed.