An adaptive learning approach for noisy data streams

Proceedings - Fourth IEEE International Conference on Data Mining, ICDM 2004

Volumn , Issue , 2004, Pages 351-354

  Chu, Fang ^a   Wang, Yizhou ^a   Zaniolo, Carlo ^a  

^a University of California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE LEARNING; DATA SETS; MODEL LEARNING; NOISY DATA STREAMS;

ADAPTIVE SYSTEMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; MATHEMATICAL TRANSFORMATIONS; MAXIMUM LIKELIHOOD ESTIMATION; ROBUSTNESS (CONTROL SYSTEMS); SET THEORY; STATISTICAL METHODS;

DATA MINING;

EID: 19544389988     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (5)

1. J. Bilmes. A gentle tutorial on the em algorithm and its application to parameter estimation for gaussian mixture and hidden markov models. In Technical Report ICSI-TR-97-021, 1998.
2. F. Chu, Y. Wang, and C. Zaniolo. An adaptive learning approach for noisy data streams. In Technical report 040029, UCLA Computer Science, 2004.
3. T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning, Data Mining, Inference and Prediction. Springer, 2000.
4. W. Street and Y. Kim. A streaming ensemble algorithm (sea) for large-scale classification. In Int'l Conf. on Knowledge Discovery and Data Mining (SIGKDD), 2001.
5. H. Wang, W. Fan, P. Yu, and J. Han. Mining concept-drifting data streams using ensemble classifiers. In Int'l Conf. on Knowledge Discovery and Data Mining (SIGKDD), 2003.