一种基于遗传神经网络文本分类器的研究

A Survey on Chinese Text Classifier Based on Genetic-Neural Networks Algorithm

本文依据隐马尔柯夫模型修正特征词属性向量，利用BP神经网络的学习能力，采用遗传算法优化，构造了一种遗传神经网络的中文文本分类器。实验表明，此文本分类器分类有较高的准确性。

This paper studies the attribute vectors of key words are amended by using Hidden Markov Models （HMM）. Create a Chinese text classifier based on genetic and neural networks with the help of BP neural networks＇ learning ability and networks are optimized by genetic algorithm （GA）. The results of the experiment show that this text classifier has presented high classification precision.