Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Machine learning in automated text categorization
ACM Computing Surveys (CSUR)
Training products of experts by minimizing contrastive divergence
Neural Computation
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
A Comparative Study on Feature Selection in Text Categorization
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
The Journal of Machine Learning Research
A fast learning algorithm for deep belief nets
Neural Computation
International Journal of Approximate Reasoning
Learning Deep Architectures for AI
Foundations and Trends® in Machine Learning
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A novel text classification approach is proposed in this paper based on deep belief network. Deep belief network constructs a deep architecture to obtain the high level abstraction of input data, which can be used to model the semantic correlation among words of documents. After basic features are selected by statistical feature selection measures, a deep belief network with discriminative fine tuning strategy is built on basic features to learn high level deep features. A support vector machine is then trained on the learned deep features. The proposed method outperforms traditional classifier based on support vector machine. As a dimension reduction strategy, the deep belief network also outperforms the traditional latent semantic indexing method. Detailed experiments are also made to show the effect of different fine tuning strategies and network structures on the performance of deep belief network.