Change detection with Kalman filter and CUSUM

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6202 LNAI, Issue , 2010, Pages 148-162

  Severo, Milton ^a,b   Gama, João ^a  

^a UNIVERSITY OF PORTO   (Portugal)

^b University of Porto   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

CHANGE DETECTION; CHANGE DETECTORS; CUMULATIVE SUMS; DATA SETS; DECISION MODELS; DETECTION SYSTEM; DYNAMIC ENVIRONMENTS; FALSE ALARMS; HIGH PROBABILITY; INCREMENTAL LEARNING; POST-PROCESSOR; REAL-WORLD APPLICATION; REAL-WORLD DATASETS; REGRESSION MODEL; REGRESSION PROBLEM; STATIONARY DISTRIBUTION;

DETECTORS; KALMAN FILTERS; REGRESSION ANALYSIS;

LEARNING ALGORITHMS;

EID: 78549289259     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-16392-0_9     Document Type: Conference Paper

References (20)

1. Andre, D., Stone, P.: Physiological data modeling contest. Technical report, University of Texas at Austin (2004)
2. Basseville, M., Nikiforov, I.: Detection of Abrupt Changes: Theory and Applications. Prentice-Hall, Englewood Cliffs (1993)
3. Bhattacharyya, G., Johnson, R.: Statistical Concepts and Methods. John Willey & Sons, New York (1977)
4. Bianchi, G., Tinnirello, I.: Kalman filter estimation of the number of competing terminals in IEEE. In: The 22nd Annual Joint Conference of IEEE Computer and Communications (2003)
5. Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. Advances in Neural Information Processing Systems 13 (2001)
6. Domingos, P., Hulten, G.: Mining high-speed data streams. In: Knowledge Discovery and Data Mining, pp. 71-80 (2000)
7. Flemons, W.W., Littner, M.R., Rowley, J.A., Anderson, W.M., Gay, P., Hudgel, D.W., McEvoy, R.D., Loube, D.I.: Home diagnosis of sleep apnoeas: A systematic review of the literature. Chest, 1543-1579, 211-237 (2003)
8. Wald, A.: Sequential Analysis. John Wiley and Sons, Inc., Chichester (1947)
9. Friedman, J.: Multivariate adaptive regression splines. Annals of Statistics 19(1), 1-141 (1991)
10. Gama, J., Medas, P., Castillo, G.: Learning with drift detection. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 286-295. Springer, Heidelberg (2004)
11. Grant, E., Leavenworth, R.: Statistical Quality Control. McGraw-Hill, New York (1996)
12. Guimares, G., Peter, J.H., Penzel, T., Ultsch, A.: A method for automated temporal knowledge acquisition applied to sleep-related breathing disorders. Artificial Intelligence in Medicine 23, 211-237 (2001) (Pubitemid 33009451)
13. Higgins, C.M., Goodman, R.M.: Incremental learning using rule-based neural networks. In: International Joint Conference on Neural Networks, Seattle, WA, pp. 875-880 (1991)
14. Hulten, G., Spencer, L., Domingos, P.: Mining time-changing data streams. In: Proceedings of Knowledge Discovery and Data Mining. ACM Press, New York (2001)
15. Kalman, R.E.: A new approach to linear filtering and prediction problems. Transaction of ASME - Journal of Basic Engineering, 35-45 (1960)
16. Page, E.S.: Continuous inspection schemes. Biometrika 41(1/2), 100-115 (1954)
17. Pang, K.P., Ting, K.M.: Improving the centered CUSUMs statistic for structural break detection in time series. In: Proc. 17th Australian Joint Conference on Artificial Intelligence. LNCS. Springer, Heidelberg (2004)
18. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria (2005), ISBN: 3-900051-07-0
19. Rojas, R.: The Kalman filter. Technical report, Freie University of Berlin (2003)
20. Welch, G., Bishop, G.: An introduction to the Kalman filter. Technical report, 95-041, Department of Computer Science, Department of Computer Science, University of North Caroline, Chapel Hill (April 2004)