A bias/variance decomposition for models using collective inference

Machine Learning

Volumn 73, Issue 1, 2008, Pages 87-106

  Neville, Jennifer ^a   Jensen, David ^b  

^a PURDUE UNIVERSITY   (United States)

^b Biologically Inspired Neural and Dynamical Systems Laboratory   (United States)

Author keywords

Collective inference; Evaluation; Statistical relational learning

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER PROGRAMMING; EDUCATION; ERRORS; INFERENCE ENGINES; LEARNING ALGORITHMS; LEARNING SYSTEMS; LOGIC PROGRAMMING;

APPROXIMATE INFERENCE; COLLECTIVE INFERENCE; DATA CHARACTERISTICS; EVALUATION; INDUCTIVE LOGIC PROGRAMMING; INFERENCE PROCESS; LEARNING PROCESSES; MACHINE-LEARNING ALGORITHMS; MODEL PERFORMANCE; REAL-WORLD DATASETS; RELATIONAL MODELING; STATISTICAL RELATIONAL LEARNING; TEST SETS;

ERROR ANALYSIS;

EID: 50649124443     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-008-5066-6     Document Type: Article

References (23)

1. Bernstein, A., Clearwater, S., & Provost, F. (2003). The relational vector-space model and industry classification. In Proceedings of the IJCAI-2003 workshop on learning statistical models from relational data (pp. 8-18).
2. Craven, M., DiPasquo, D., Freitag, D., McCallum, A., Mitchell, T., Nigam, K., & Slattery, S. (1998). Learning to extract symbolic knowledge from the world wide web. In Proceedings of the 15th national conference on artificial intelligence (pp. 509-516).
3. Domingos, P. (2000). A unified bias-variance decomposition for zero-one and squared loss. In Proceedings of the 17th national conference on artificial intelligence (pp. 564-569).
4. Domingos, P., & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 103-130.
5. Duda, R., Hart, P., & Stork, D. (2001). Pattern classification. New York: Wiley.
6. Friedman, J. (1997). On bias, variance, 0/1-loss, and the curse-of-dimensionality. Data Mining and Knowledge Discovery, 1(1), 55-77.
7. Geman, S., Bienenstock, E., & Doursat, R. (1992). Neural networks and the bias/variance dilemma. Neural Computation, 4, 1-58.
8. Getoor, L., Friedman, N., Koller, D., & Pfeffer, A. (2001). Learning probabilistic relational models. In Relational data mining (pp. 307-335). Berlin: Springer.
9. Goodman, L. (1961). Snowball Sampling. Annals of Mathematical Statistics, 32, 148-170.
10. Heckerman, D., Chickering, D., Meek, C., Rounthwaite, R., & Kadie, C. (2000). Dependency networks for inference, collaborative filtering and data visualization. Journal of Machine Learning Research, 1, 49-75.
11. Hill, S., Provost, F., & Volinsky, C. (2006). Network-based marketing: identifying likely adopters via consumer networks. Statistical Science, 22(2).
12. Holte, R. (1993). Very simple classification rules perform well on most commonly used datatsets. Machine Learning, 11, 63-91.
13. James, G. (2003). Variance and bias for general loss functions. Machine Learning, 51, 115-135.
14. Jensen, D., & Neville, J. (2002). Linkage and autocorrelation cause feature selection bias in relational learning. In Proceedings of the 19th international conference on machine learning (pp. 259-266).
15. Jensen, D., Neville, J., & Gallagher, B. (2004). Why collective inference improves relational classification. In Proceedings of the 10th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 593-598).
16. Macskassy, S., & Provost, F. (2007). Classification in networked data: a toolkit and a univariate case study. Journal of Machine Learning Research, 8, 935-983.
17. McCallum, A., Nigam, K., Rennie, J., & Seymore, K. (1999). A machine learning approach to building domain-specific search engines. In Proceedings of the 16th international joint conference on artificial intelligence (pp. 662-667).
18. Murphy, K., Weiss, Y., & Jordan, M. (1999). Loopy belief propagation for approximate inference: an empirical study. In Proceedings of the 15th conference on uncertainty in artificial intelligence (pp. 467-479).
19. Neville, J., & Jensen, D. (2004). Dependency networks for relational data. In Proceedings of the 4th IEEE international conference on data mining (pp. 170-177).
20. Neville, J., & Jensen, D. (2005). Leveraging relational autocorrelation with latent group models. In Proceedings of the 5th IEEE international conference on data mining (pp. 322-329).
21. Neville, J., Jensen, D., Friedland, L., & Hay, M. (2003). Learning relational probability trees. In Proceedings of the 9th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 625-630).
22. Taskar, B., Abbeel, P., & Koller, D. (2002). Discriminative probabilistic models for relational data. In Proceedings of the 18th conference on uncertainty in artificial intelligence (pp. 485-492).
23. Wainwright, M. (2005). Estimating the "wrong" Markov random field: benefits in the computation-limited setting. In Advances in neural information processing systems.