Classification-Aware Hidden-Web Text Database Selection
Victor Fernandezにより
1. 2.1 Database Selection Algorithms
2. 2.2 Uniform Probing for Content Summary Construction
3. 2.3 Focused Probing for Database Classification
3.1. 3. CONSTRUCTING APPROXIMATE CONTENT SUMMARIES
3.1.1. Fig 2
3.1.2. log( P ) = P 1 log( | S | ) + P 2 B = B 1 log( | S | ) + B 2
4. 3.1 Classification-Based Document Sampling
5. 3.2 Estimating Absolute Document Frequencies
5.1. 4. DATABASE SELECTION WITH SPARSE CONTENT SUMMARIES
5.1.1. Fig 6
5.1.2. Fig 7
5.1.3. Fig 8
5.1.4. Fig 9
5.1.5. Fig 10
6. 4.1 Hierarchical Database Selection
7. 4.2 Shrinkage-Based Database Selection
7.1. 5. EXPERIMENTAL SETTING
8. 5.1 Datasets
9. 5.2 Content Summary Construction Algorithms
10. 5.3 Database Selection Algorithms
10.1. 6. EXPERIMENTAL RESULTS FOR CONTENT SUMMARY QUALITY
11. 6.1 Effect of Sampling Algorithm
12. 6.2 Relationship Between Content Summaries and Categories
13. 6.3 Effect of Shrinkage
13.1. 7. EXPERIMENTAL RESULTS FOR DATABASE SELECTION ACCURACY
13.2. 8. RELATED WORK
14. 8.1 Database Selection
15. 8.2 Constructing Database Content Summaries
16. 8.3 Miscellaneous Applications of Query Probing
16.1. 9. CONCLUSION
16.2. APPENDIXES A. ESTIMATING SCORE DISTRIBUTIONS
16.3. B. ESTIMATING SCORE VARIANCE