The Research Of Web Pages Classification Based On RBF Neural Network

With the popularity of the Internet, the Internet has become the main way people get information. Web pages classification can analyze and organize massive web pages quickly and efficiently, it is a kind of machine learning methods that assign labels to web pages automatically. Among the many web pages classification algorithms, RBF neural network become a research focus in machine learning because of its excellent classification ability.This thesis describes the process of Web pages classification, the development of RBF neural network, related technologies, summarizes the important role of RBF neural network in web pages classification. The common training methods of RBF are also studied, including the derived multi-instance multi-label RBF neural network. We proposed an improved method for the poor performance of MIMLRBF on unbalanced dataset. This method takes into account the overall distribution of the samples, so that the hidden neurons generated on all classes tends to balance, reducing the unbalance problem on the network.When the training data are noisy or not easily discernible, the SVD method will cause augmented overall error in network performance. In this thesis, the weights optimization method based on the steepest descent method is proposed for relieve this problem. Firstly, the weight matrix is initialized by SVD method, and then optimized by steepest descent method. The learning rate matrix is computed by minimizing the sum-squared error function of the new weight matrix. The performance of network is improved on noisy training data.Finally, the improved training algorithms are applied to the web pages classification system. The performance of improved algorithms are analyzed and compared. Experimental data show that the algorithms have higher efficiency and accuracy.