| Summary: | 碩士 === 國立臺北科技大學 === 資訊工程系研究所 === 101 === Artificial neural network (ANN) has been applied to text classification. However, comparing with other classification methods, ANN needs enormous memory space and training time to build the model. This makes ANN infeasible in practical applications. In this paper, we try to adopt the ideas of human neural system and learning style, and combine with the existing models of ANN. We propose a humanized neural network architecture that is based on human intelligence. In this architecture, a neural network with multiple abilities to solve numerous problems is presented, and with its unsupervised learning algorithm.
In our experiment, Reuters-21578 was used as the dataset to show the effect of the proposed architecture on text classification. The experiment result showed that HNN can effectively classify texts with the best F1-measure of 92.5%. It also showed the learning algorithm can enhance the accuracy effectively and efficiently. With the expansion of the architecture, the training time and test time shows a linear growth. Comparing with ANN, HNN has better scalability and practicality.
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