Tracking concept drifting with gaussian mixture model

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5960, Issue 3, 2005, Pages 1562-1570

  Wu, Jun ^a   Hua, Xian Sheng ^b   Zhang, Bo ^a  

^a TSINGHUA UNIVERSITY   (China)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

Batch learning; Concept drifting; Gaussian Mixture Model

Indexed keywords

DATA REDUCTION; LEARNING SYSTEMS; LIFE CYCLE; MATHEMATICAL MODELS; PARAMETER ESTIMATION;

BATCH LEARNING; CONCEPT DRIFTING; GAUSSIAN MIXTURE MODEL;

GAUSSIAN NOISE (ELECTRONIC);

EID: 32544444980     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.632730     Document Type: Conference Paper

References (11)

1. Alexey Tsymbal, The problem of concept drift: definitions and related work, can be available from website: http://www.cs.tcd.ie/publications/tech- reports/reports.04/TCD-CS-2004-15.pdf.
2. Widmer G., Kubat M., Learning in the presence of concept drifting and hidden contexts, Machine Learning, 23 (1) 1996, 69-101.
3. Ralf Klinkenberg, Thorsten Joachims, Detecting Concept Drift with Support Vector Machines. In Langley, Pat (editors), Proceedings of the Seventeenth International Conference on Machine Learning (ICML), pages 487-494, San Francisco, CA, USA, Morgan Kaufmann, 2000.
4. Klinkenberg, Ralf. Using Labeled and Unlabeled Data to Learn Drifting Concepts. In Kubat, Miroslav and Morik, Katharina (editors), Workshop notes of the IJCAI-01 Workshop on Learning from Temporal and Spatial Data, pages 16 - 24, IJCAI, Menlo Park, CA, USA, AAAI Press, 2001.
5. Wei Fan, Systematic Data Selection to Mine Concept-Drifting Data Streamsm, KDD04, Research Track Paper.
6. Wei Fan, StreamMiner: A Classifier Ensemble-based Engine to Mine Concept Drifting, Proceedings of the 30th VLDB Conference, Toronto, Canada, 2004
7. Haixun Wang, Wei Fan, Philip S. Yu, Jiawei Han, Mining Concept-Drifting Data Streams using Ensemble Classifiers, Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24 - 27, 2003.
8. M. Figueiredo, et al, On Fitting Mixture Models, Energy Minimization in Computer Vision and Pattern Recognition, E. Hancock and M. Pellilo (Eds.), Springer-Verlag, 1999.
9. S. Kullback, Information Theory and Statistics, J. Wiley & Sons, New York, 1959.
10. M. West, J. Harrison, Bayesian Forecasting and Dynamic Models, Springer Verlag, New York, 1989.
11. Jun Wu, et al, An Online-Optimized Incremental Learning Framework for Video Semantic Classification, ACM Multimedia 2004.