基于极限学习机的网页分类应用

极限学习机ELM不同于传统的神经网络学习算法（如BP算法），是一种高效的单隐层前馈神经网络（SLFNs）学习算法。将极限学习机引入到中文网页分类任务中。对中文网页进行预处理，提取其特性信息，从而形成网页特征树，产生定长编码作为极限学习机的输入数据。实验结果表明该方法能够有效地分类网页。

Classification of web pages based on extreme learning machine

ELM extreme learning machine is different from traditional neural network learning algorithm （such as BP algorithm）, is a highly efficient Single hidden Layer Feedforward Neural network （SLFNs） learning algorithm. In this paper, ELM is introduced to Chinese web page classification task. Trait tree of web page is formed after pre-processing the Chinese web and extracting its characteristic information. Fixed-length coding is produced and took as input data of ELM. Experimental results show that the method can effectively classify web pages.