Subject Categorization for Web Educational Resources using MLP

The purpose of this study is to develop subject categorization methods for educational resources using multilayer perceptron (MLP) and to examine the performance of the test documents as an application system. To examine the performance two methods are examined: Latent Semantic Indexing method (LSI) and a three layer feedforward network as a simple MLP. The document vectors were estimated by the term feature vectors which were extracted from the teaching guidelines based on the singular value decomposition method (SVD). Comparing recall and precision rates and F1 measure for the subject categorization, the categorization performance using MLP showed better than using LSI.