Boosting expert ensembles for rapid concept recall

Proceedings of the National Conference on Artificial Intelligence

Volumn 1, Issue , 2006, Pages 464-469

  Rettinger, Achim ^a   Zinkevich, Martin ^b   Bowling, Michael ^b  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; DATA STRUCTURES; DATA TRANSFER; LEARNING ALGORITHMS; PROBLEM SOLVING;

INDUCTIVE TRANSFER; LEARNING TASKS; SIMULATED ROBOT SOCCER TASKS; SYNTHETIC DATA;

LEARNING SYSTEMS;

EID: 33750718156     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (20)

1. Bowling, M. 2003. Multiagent learning in the presence of agents with limitations. CMU's Technical Report CMU-CS-03-118, CMU.
2. Caruana, R. 1997. Multitask learning. Machine Learning 28(1):41-75.
3. Freund, Y., and Schapire, R. E. 1995. A decisiontheoretic generalization of on-line learning and an application to boosting. In European Conference on Computational Learning Theory, 23-37.
4. Herbster, M., and Warmuth, M. K. 1998. Tracking the best expert. Mach. Learn. 32(2):151-178.
5. Hulten, G.; Spencer, L.; and Domingos, P. 2001. Mining time-changing data streams. In KDD '0I: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 97-106. New York, NY, USA: ACM Press.
6. Kivinen, J. 2003. Online learning of linear classifiers. In Advanced lectures on machine learning. New York, NY, USA: Springer-Verlag New York, Inc. 235-257.
7. Klinkenberg, R., and Joachims, T. 2000. Detecting concept drift with support vector machines. In ICML '00: Proceedings of the Seventeenth International Conference on Machine Learning, 487-494. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
8. Klinkenberg, R. 2004. Learning drifting concepts: Example selection vs. example weighting. Learning Drifting Concepts: Example Selection vs. Example Weighting 8(3):281-300.
9. Kolter, J. Z., and Maloof, M. A. 2005. Using additive expert ensembles to cope with concept drift. In Proceedings of the Twenty-second International Conference on Machine Learning, 449-456. New York, NY: ACM Press.
10. Littlestone, N., and Warmuth, M. K. 1994. The weighted majority algorithm. Inf. Comput. 108(2):212-261.
11. Marx, Z.; Rosenstein, M.; Kaelbling, L.; and Dietterich, T. 2005. Transfer learning with an ensemble of background tasks. In Workshop on Inductive Transfer 10 Years Later held during NIPS 2005.
12. Obst, O., and Rollmann, M. 2005. SPARK - A Generic Simulator for Physical Multiagent Simulations. Computer Systems Science and Engineering 20(5):347-356.
13. Riley, P., and Veloso, M. 2001. Recognizing probabilistic opponent movement models. In Birk, A.; Coradeschi, S.; and Tadokoro, S., eds., Robot Soccer World Cup V, 453 458. Berlin: RoboCup Federation.
14. Rüping, S. 2001. Incremental learning with support vector machines. In ICDM '01: Proceedings of the 2001 IEEE International Conference on Data Mining, 641-642. Washington, DC, USA: IEEE Computer Society.
15. Schlimmer, J., and Granger, R. 1986. Beyond incremental processing: Tracking concept drift. In Proceedings of the 5th National Conference on Artificial Intelligence, 502-507. Menlo Park: AAAI Press.
16. Silver, D. L., and Mercer, R. E. 1998. The parallel transfer of task knowledge using dynamic learning rates based on a measure of relatedness. 213-233.
17. Silver, D., and Mercer, R. 2001. Selective functional transfer: Inductive bias from related tasks. In Hamza, M., ed., IASTED International Conference on Artificial Intelligence and Soft Computing (ASC200I), 182-189. ACTA Press.
18. Stone, P., and Veloso, M. 1996. Beating a defender in robotic soccer: Memory-based learning of a continuous function. In Touretzky, D. S.; Mozer, M. C.; and Hasselmo, M. E., eds., Advances in Neural Information Processing Systems 8, 896-902. Cambridge, MA: MIT Press.
19. Vovk, V. G. 1990. Aggregating strategies. In COLT '90: Proceedings of the third annual workshop on Computational learning theory, 371-386. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
20. Wu, P., and Dietterich, T. G. 2004. Improving svm accuracy by training on auxiliary data sources. In ICML '04: Proceedings of the twenty-first international conference on Machine learning, 110. New York, NY, USA: ACM Press.