基于量子自组织神经网络的Deep Web分类方法研究

针对Deep Web数据源主题分类问题,首先研究了不同位置的特征项对Deep Web接口领域分类的影响,提出一种基于分级权重的特征选择方法RankFW;然后提出一种依赖领域知识的量子自组织特征映射神经网络模型DR-QSOFM及其分类算法,该模型在训练的不同阶段对特征向量和目标向量产生不同程度的依赖,使竞争层中获胜神经元...

Research on Deep Web Classification Approach Based on Quantum Self-organization Feature Mapping Network

In order to solve the problem of Deep Web data sources classification, this paper firstly researched how fealures in different position could effect the domain of Deep Web interfaces, and proposed a feature selection method RankF W which is based on Ranked weights. Then, a quantum self-organization feature mapping network model was proposed with a classification algorithm This model relies on the feature vectors and target vectors incoordinately in different phases of training, making a more centralized distribution of winner neurons in competition layer and more obvious boundaries among clusters. Finally, some experiments were designed and carried out on the expanded TEL-8 dataset to test the validity of RankFW and DR-QSOFM.