Name
Abstract | ||
|---|---|---|
The purpose of this study is to develop subject categoriza- tion 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 sin- gular 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. |
Year | Venue | Keywords |
|---|---|---|
2003 | ESANN | latent semantic indexing,multilayer perceptron,feature vector |
Field | DocType | Citations |
Singular value decomposition,Categorization,Educational resources,Feature vector,Pattern recognition,Computer science,Precision and recall,Decomposition method (constraint satisfaction),Multilayer perceptron,Artificial intelligence,Machine learning,Feed forward | Conference | 3 |
PageRank | References | Authors |
0.43 | 3 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minoru Nakayama | 1 | 62 | 13.64 |
| Yasutaka Shimizu | 2 | 80 | 16.88 |