메뉴 건너뛰기




Volumn 4472 LNCS, Issue , 2007, Pages 490-500

An ensemble approach for incremental learning in nonstationary environments

Author keywords

Concept drift; Learn++.NSE; Nonstationary environment

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER SIMULATION; DATA REDUCTION; DISTRIBUTION FUNCTIONS; DYNAMIC PROGRAMMING;

EID: 34548217482     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-72523-7_49     Document Type: Conference Paper
Times cited : (44)

References (19)
  • 1
    • 0010012318 scopus 로고
    • Incremental Learning from Noisy Data
    • Schlimmer, J. C. and Granger, R. H.; Incremental Learning from Noisy Data. Machine Learning 1 (1986) 317-354.
    • (1986) Machine Learning , vol.1 , pp. 317-354
    • Schlimmer, J.C.1    Granger, R.H.2
  • 2
    • 0002896413 scopus 로고
    • Tracking drifting concepts by minimizing disagreements
    • Helmbold, D. P. and Long, P. M.; Tracking drifting concepts by minimizing disagreements. Machine Learning 14 (1994) 27-45.
    • (1994) Machine Learning , vol.14 , pp. 27-45
    • Helmbold, D.P.1    Long, P.M.2
  • 3
    • 0030126609 scopus 로고    scopus 로고
    • Learning in the presence of concept drift and hidden contexts
    • Widmer, G. and Kubat, M.; Learning in the presence of concept drift and hidden contexts. Machine Learning 23 (1996) 69-101.
    • (1996) Machine Learning , vol.23 , pp. 69-101
    • Widmer, G.1    Kubat, M.2
  • 6
    • 0348216539 scopus 로고    scopus 로고
    • Adaptation to Drifting Concepts. Progress in Artificial Intelligence
    • Castillo, G., Gama, J., and Medas, P.; Adaptation to Drifting Concepts. Progress in Artificial Intelligence, Lecture Notes in Computer Science 2902 (2003) 279-293.
    • (2003) Lecture Notes in Computer Science , vol.2902 , pp. 279-293
    • Castillo, G.1    Gama, J.2    Medas, P.3
  • 7
    • 33749618778 scopus 로고    scopus 로고
    • Learning with Drift Detection. Advances in Artificial Intelligence - SBIA 2004
    • Gama, J., Medas, P., Castillo, G., and Rodrigues, P.; Learning with Drift Detection. Advances in Artificial Intelligence - SBIA 2004, Lecture Notes in Computer Science 3171 (2004) 286-295.
    • (2004) Lecture Notes in Computer Science , vol.3171 , pp. 286-295
    • Gama, J.1    Medas, P.2    Castillo, G.3    Rodrigues, P.4
  • 9
    • 1242310003 scopus 로고    scopus 로고
    • Incremental learning with partial instance memory
    • Maloof, M. A. and Michalski, R. S.; Incremental learning with partial instance memory. Artificial Intelligence 154 (2004) 95-126.
    • (2004) Artificial Intelligence , vol.154 , pp. 95-126
    • Maloof, M.A.1    Michalski, R.S.2
  • 10
    • 24644449205 scopus 로고    scopus 로고
    • Learning classification rules for telecom customer call data under concept drift. Soft Computing - A Fusion of Foundations
    • Black, M. and Hickey, R.; Learning classification rules for telecom customer call data under concept drift. Soft Computing - A Fusion of Foundations, Methodologies and Applications 8 (2003) 102-108.
    • (2003) Methodologies and Applications , vol.8 , pp. 102-108
    • Black, M.1    Hickey, R.2
  • 11
    • 2542525075 scopus 로고    scopus 로고
    • Adaptive probabilistic neural networks for pattern classification in time-varying environment
    • Rutkowski, L.; Adaptive probabilistic neural networks for pattern classification in time-varying environment. IEEE Transactions on Neural Networks 15 (2004) 811-827.
    • (2004) IEEE Transactions on Neural Networks , vol.15 , pp. 811-827
    • Rutkowski, L.1
  • 12
    • 6344221942 scopus 로고    scopus 로고
    • Encoding a priori information in neural networks to improve its modeling performance under non-stationary environment
    • Cheng-Kui, G., Zheng-Ou, W., and Ya-Ming, S.; Encoding a priori information in neural networks to improve its modeling performance under non-stationary environment. International Conference on Machine Learning and Cybernetics (ICMLC 2004) 5 (2004) 3068-3072.
    • (2004) International Conference on Machine Learning and Cybernetics (ICMLC , vol.5 , pp. 3068-3072
    • Cheng-Kui, G.1    Zheng-Ou, W.2    Ya-Ming, S.3
  • 13
    • 35048891979 scopus 로고    scopus 로고
    • Classifier Ensembles for Changing Environments. Multiple Classifier Systems (MCS 2004)
    • Kuncheva, L. I.; Classifier Ensembles for Changing Environments. Multiple Classifier Systems (MCS 2004), Lecture Notes in Computer Science 3077 (2004) 1-15.
    • (2004) Lecture Notes in Computer Science , vol.3077 , pp. 1-15
    • Kuncheva, L.I.1
  • 14
    • 0030819669 scopus 로고    scopus 로고
    • Empirical Support for Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain
    • Blum, A.; Empirical Support for Winnow and Weighted-Majority Algorithms: Results on a Calendar Scheduling Domain. Machine Learning 26 (1997) 5-23.
    • (1997) Machine Learning , vol.26 , pp. 5-23
    • Blum, A.1
  • 15
    • 1942483164 scopus 로고    scopus 로고
    • Ph.D. Dissertation, University of California, Berkeley
    • Oza, N.; Online Ensemble Learning, Ph.D. Dissertation, (2001) University of California, Berkeley.
    • (2001) Online Ensemble Learning
    • Oza, N.1
  • 16
    • 26444530040 scopus 로고    scopus 로고
    • ACE: Adaptive Classifiers-Ensemble System for Concept-Drifting Environments
    • Multiple Classifier Systems MCS
    • Kyosuke, N., Koichiro, Y., and Takashi, O.; ACE: Adaptive Classifiers-Ensemble System for Concept-Drifting Environments. Multiple Classifier Systems (MCS 2005), Lecture Notes in Computer Science 3541 (2005) 176-185.
    • (2005) Lecture Notes in Computer Science , vol.3541 , pp. 176-185
    • Kyosuke, N.1    Koichiro, Y.2    Takashi, O.3
  • 18
    • 7444250934 scopus 로고    scopus 로고
    • Fast and Light Boosting for Adaptive Mining of Data Streams. Advances in Knowledge Discovery and Data Mining
    • Chu, F. and Zaniolo, C.; Fast and Light Boosting for Adaptive Mining of Data Streams. Advances in Knowledge Discovery and Data Mining, Lecture Notes in Computer Science 3056 (2004) 282-292.
    • (2004) Lecture Notes in Computer Science , vol.3056 , pp. 282-292
    • Chu, F.1    Zaniolo, C.2
  • 19
    • 0035521110 scopus 로고    scopus 로고
    • Learn++: An incremental learning algorithm for supervised neural networks. Systems, Man and Cybernetics, Part C
    • Polikar, R., Upda, L., Upda, S. S., and Honavar, V.; Learn++: an incremental learning algorithm for supervised neural networks. Systems, Man and Cybernetics, Part C, IEEE Transactions on 31 (2001) 497-508.
    • (2001) IEEE Transactions on 31 , pp. 497-508
    • Polikar, R.1    Upda, L.2    Upda, S.S.3    Honavar, V.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.