Learning from aggregate views

Proceedings - International Conference on Data Engineering

Volumn 2006, Issue , 2006, Pages 3-

  Chen, Bee Chung ^a   Chen, Lei ^a   Ramakrishnan, Raghu ^a   Musicant, David R ^b  

^a UNIVERSITY OF WISCONSIN MADISON   (United States)

^b CARLETON COLLEGE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FRAMEWORK; RESTRICTION FREE AGGREGATE (RFA); TRADITIONAL PROBLEM OF LEARNING;

DATA ACQUISITION; DATA MINING; PERSONNEL TRAINING; PROBLEM SOLVING;

LEARNING SYSTEMS;

EID: 33749643147     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2006.86     Document Type: Conference Paper

References (34)

1. N.R. Adam and J.C. Wortmann. Security-Control Methods for Statistical Databases: A Comparative Study, ACM Computing Surveys, 1989.
2. W.P. Bergsma and T. Rudas. Marginal Models for Categorical Data, Annals of Statistics, 2002.
3. Y. Bishop. Discrete Multivariate Analysis: Theory and Practice, MIT Press, 1977.
4. A. Blum and T. Mitchell. Combining labeled and unlabeled data with co-training, COLT, 1998.
5. L. Breiman. Bagging Predictors. Machine Learning, 1996.
6. L. Breiman. Random Forests. Machine Learning, 2001.
7. L. Chen, Z. Huang and R. Ramakrishnan. Cost-based Labeling of Groups of Mass Spectra. SIGMOD, 2004.
8. G.F. Cooper, E.Herskovits. A Bayesian Method for the Induction of Probabilistic Networks from Data, Machine Learning, 1992.
9. Eli Dichterman. Learning with Limited Visibility. CDAM Research Report, LSE-CDAM-98-0, 1998.
10. A. Dobra and S.E. Fienberg. Bounds for cell entries in contingency tables induced by fixed marginal totals with applications to disclosure limitation. Stat. J. of the UNECE, 2001.
11. P. Domingos and M. Pazzani. On the Optimality of the Simple Bayesian Classifier under 0-1 Loss, Machine Learning, 1997.
12. P. Domingos and G. Hulten. Mining Hight-Speed Data Streams. SIGKDD, 2000.
13. S. Dzeroski and N. Lavrac. Relational Data Mining, 2001.
14. Y. Freund and R.E. Schapire. Experiments with a New Boosting Algorithm. ICML, 1996.
15. N. Friedman, D. Geiger and M. Goldszmidt. Bayesian network classifiers, Machine Learning, 1997.
16. N. Friedman, L. Getoor, D. Koller and A. Pfeffer. Learning Probabilistic Relational Models, IJCAI, 1999.
17. A. Garg and D. Roth. Understanding Probabilistic Classifiers, EMCL 2001.
18. J. Gehrke, V. Ganti, R. Ramakrishnan and W-Y. Loh. BOAT - Optimistic Decision Tree Construction, SIGMOD, 1999.
19. L. Getoor, D. Koller, B. Taskar and N. Friedman. Learning probabilistic relational models with structure uncertainty. AAAI Workshop on learning statistical models from relational data, 2000.
20. L.K. Hansen and P. Salamon. Neural Network Ensembles. IEEE Trans. on PAMI, 1990.
21. M. Kearns. Efficient Noise-Tolerant Learning from Statistical Queries. J. ACM, 1998.
22. R. Jiroušek and S. Přečil. On the Effective Implementation of the Iterative Proportional Fitting Procedure, Computational Statistics & Data Analysis, 1995.
23. A.J. Knobbe, A. Siebes and B. Marseille. Involving Aggregate Functions in Multirelational Search, PKDD, 2002.
24. S.R. Madden, M.J. Franklin, J.M. Hellerstein, and W. Hong. The Design of an Acquisitional Query Processor for Sensor Networks. SIGMOD, 2003.
25. G.M. Fung, O.L. Mangasarian and J.W. Shavlik. Knowledge-based Support Vector Machine Classifiers. NIPS 2002.
26. I. Muslea, S. Minton and C.A. Knoblock. Active + Semi-supervised Learning = Robust Multi-View Learning, ICML 2002.
27. C. Perlich and F. Provost. Aggregation-Based Feature Invention and Relational Concept Classes, SIGKDD 2003.
28. F. Provost and P. Domingos. Tree Induction for Probability-based Ranking, Machine Learning, 2003.
29. J.R. Quinlan. C4.5: Programs for Machine Learning, Morgan Kaufmann, 1993.
30. P. Samarati and L. Sweeney. Generalizing Data to Provide Anonymity when Disclosing Information, PODS, 1998.
31. A.B. Slavkovic and S.E. Fienberg. Bounds for Cell Entries in 2-Way Tables Given Conditional Relation Frequencies. PSD, 2004.
32. Y.W. Teh and M. Welling. On Improving the Efficiency of the Iterative Proportional Fitting Procedure, AIStats,2003.
33. I.H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools with Java Impl., Morgan Kaufmann, 2000.
34. B. Zadrozny and C. Elkan. Obtaining Probability Estimates from Decision Trees and Naïve Bayesian Classifiers, ICML, 2001.