Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching

Artificial Intelligence Review

Volumn 11, Issue 1-5, 1997, Pages 133-155

  Salganicoff, Marcos ^a  

^a Applied Science and Engineering Laboratories   (United States)

Author keywords

Concept drift; Lazy learning; Nearest neighbor learning; Non stationary function; Time varying functions

Indexed keywords

ERROR ANALYSIS; FUNCTIONS; LEARNING SYSTEMS; PATTERN RECOGNITION SYSTEMS; TIME VARYING SYSTEMS;

CLASSIFICATION ACCURACY; LAZY LEARNING; LOCALLY WEIGHTED FORGETTING (LWF) ALGORITHMS; PREDICTION ERROR CONTEXT SWITCHING (PECS); TIME WINDOWED FORGETTING (TWF) ALGORITHMS;

LEARNING ALGORITHMS;

EID: 0031070068     PISSN: 02692821     EISSN: None     Source Type: Journal    
DOI: 10.1007/978-94-017-2053-3_5     Document Type: Article

References (20)

1. Aha, D. W., Kibler, D. & Albert, M. K. (1991). Instance-Based Learning Algorithms. Machine Learning 6: 37-66.
2. Atkeson, C. G. (1991). Using Locally Weighted Regression for Robot Learning. In IEEE Conference on Robotics and Automation, 958-963. IEEE Press.
3. Blum, A. (1995). Empirical Support for Winnow and Weighted Majority Based Algorithms: Results on a Calendar Scheduling Domain. Proceedings of the Twelfth International Conference on Machine Learning, 64-72. Morgan Kaufmann.
4. Duda P. O. & Hart P. E. (1973). Pattern Classification and Scence Analysis. Wiley and Sons: New York.
5. Friedman, J. H., Bentley, J. L. & Finkel, R. A. (1977). An Algorithm for Finding Best Matches in Logarithmic Expected Time. ACM Transactions on Mathematical Software 3: 209-226.
6. Hembold, D. & Long, P. (1991). Tracking Drifting Concepts Using Random Samples. In Fourth Workship on Computational Learning Theory, 12-23.
7. Kalman, R. E. (1960). A New Approach to Linear Filtering and Prediction Problems. Transactions ASME, Journal of Basic Engineering: 35-45.
8. Kuh, A., Petsche, T. & Rivest, R. L. (1991). Learning Time-Varying Concepts. In Advances in Neural Information Processing Systems 3, 183-189. Morgan Kaufmann.
9. Larson, R. & Marx, M. (1986). An Introduction to Mathematical Statistics. Prentice Hall: Englewood Cliffs, NJ.
10. Maes, P. & Brooks, R. A. (1990). Learning to Coordinate Behaviors. In AAAI-90, 796-802.
11. McCloskey, M. & Cohen, N. J. (1989). The Psychology of Learning and Motivation, chapter Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem. Academic Press: New York.
12. Moore, A. W. (1990). Acquisition of Dynamic Control Knowledge for a Robotic Manipulator. In Proceedings of the Seventh International Conference on Machine Learning, 244-252. Morgan Kaufmann.
13. Moore, A. W. (1991). Fast, Robust Adaptive Control by Learning Only Forward Models. In Advances in Neural Information Processing Systems. Morgan Kaufman.
14. Ratcliff, R. (1990). Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions. Psychological Reviews 97: 285-308.
15. Salganicoff, M. (1992). Learning and Forgetting for Perception-Action: A Projection-Pursuit and Density-Adaptive Approach. PhD thesis, University of Pennsylvania, Philadelphia, PA, Dept. of Computer and Information Science.
16. Salganicoff, M. (1993). Density-Adaptive Learning and Forgetting. In Proceedings of the Tenth International Conference on Machine Learning. San Francisco, CA: Morgan Kaufman.
17. Schlimmer, J. C. & Granger, R. H. (1986). Incremental Learning from Noisy Data. Machine Learning 1: 317-354.
18. Valiant, L. G. (1984). A Theory of the Learnable. Communications of the ACM 27(11): 1134-1142.
19. Widmer, G. & Kubat, M. (1992). Learning Flexible Concepts from Streams of Examples: FLORA2. Proceedings of the Tenth European Conference on Artificial Intelligence, 463-467. John Wiley & Sons.
20. Widmer, G. & Kubat, M. (1992). Effective Learning in Dynamic Environments by Explicit Context Tracking (Technical Report 93-35). Vienna, Austria: Austrian Institute for Artifical Intelligence.