Study of Web document classification based on best weight neural network ensembles
基于最优权重的神经网络集成文本分类研究

Inspired by the ideas of neural network ensembles, this paper constructed a multi-BP neural network modeling with best weights that was based the strategy of minimum estimate error. To do this, according to the capability of classification of each network and the degree of each network related to other networks, the different weight would assign to the probability esti-mates of maximuma posterior ( MAP) . Further, improved the accuracy of estimate and classification. The experimental results of English database demonstrate that this model hold the better accuracy and speed than the Bayes and kNN models.