Research on development of retrieval and classification system of documents on the Web which is based on analysis of teaching text books.

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The purpose of this study is to compare performance among developed subject classification methods for educational Web re-sources.Three methods are vector space model (VSM) using degree of cosine similarity, vector distance on the self-organizing map (SOM) and three layer percptron (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).The 403 teaching schemes were prepared as the test documents for the subject discrimination. The recall rate, precision rate and Fl measure as the classification performance measure, were compared across three methods. The recall rate using MLP is the highest for most subjects. The highest precision rate among the methods depend on subject. As a result, F1 measure as total performance index of most subjects are the highest for the method using MLP.According to the results, the subject classification methods for educational Web resources by using multi layer perceptron (MLP) is tested to achieye better performance. Ten classification methods and 403 test documents were prepared, then classification perfor-mance was compared among conditions by the F1 measure.As a result, it was examined effectiveness of singular value decomposition (SVD) to extract term feature from term-document matrix as description characteristics of the national teaching guideline. The highest performance of classification method was the use of MLP which trained with all term features extracting from SVD when document feature was estimated by terms appering unique subject.Also, it was suggested that there were significant differences of document feature components between correct and incorrect classified documents.