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Volumn 18, Issue 4, 2012, Pages 299-310

Partially labeled data stream classification with the semi-supervised K-associated graph

Author keywords

Concept drift; Graph based learning; Incremental learning; Semi supervised online classification

Indexed keywords

E-LEARNING; GRAPHIC METHODS; SUPERVISED LEARNING;

EID: 84867297730     PISSN: 01046500     EISSN: 16784804     Source Type: Journal    
DOI: 10.1007/s13173-012-0072-8     Document Type: Article
Times cited : (15)

References (39)
  • 1
    • 0042378381 scopus 로고    scopus 로고
    • Laplacian eigenmaps for dimensionality reduction and data representation
    • Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15: 1373-1396.
    • (2003) Neural Comput , vol.15 , pp. 1373-1396
    • Belkin, M.1    Niyogi, P.2
  • 2
    • 36448962045 scopus 로고    scopus 로고
    • Manifold regularization: a geometric framework for learning from labeled and unlabeled examples
    • Belkin M, Niyogi P, Sindhwani V (2006) Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. J Mach Learn Res 1: 1-48.
    • (2006) J Mach Learn Res , vol.1 , pp. 1-48
    • Belkin, M.1    Niyogi, P.2    Sindhwani, V.3
  • 4
    • 80054694302 scopus 로고    scopus 로고
    • A nonparametric classification method based on K-associated graphs
    • Bertini JR Jr, Zhao L, Motta R, Lopes A (2011) A nonparametric classification method based on K-associated graphs. Inf Sci 181: 5435-5456.
    • (2011) Inf Sci , vol.181 , pp. 5435-5456
    • Bertini Jr., J.R.1    Zhao, L.2    Motta, R.3    Lopes, A.4
  • 6
    • 84874947497 scopus 로고    scopus 로고
    • Particle competition and cooperation in networks for semi-supervised learning
    • doi:10.1109/TKDE.2011.119
    • Breve FA, Zhao L, Quiles M, Pedrycz W, Liu J (2011) Particle competition and cooperation in networks for semi-supervised learning. IEEE Trans Knowl Data Eng. doi: 10. 1109/TKDE. 2011. 119.
    • (2011) IEEE Trans Knowl Data Eng
    • Breve, F.A.1    Zhao, L.2    Quiles, M.3    Pedrycz, W.4    Liu, J.5
  • 8
    • 41549144249 scopus 로고    scopus 로고
    • Optimization techniques for semi-supervised support vector machines
    • Chapelle O, Sindhwani V, Keerthi S (2008) Optimization techniques for semi-supervised support vector machines. J Mach Learn Res 9: 203-233.
    • (2008) J Mach Learn Res , vol.9 , pp. 203-233
    • Chapelle, O.1    Sindhwani, V.2    Keerthi, S.3
  • 12
    • 34548118248 scopus 로고    scopus 로고
    • Offline/realtime traffic classification using semi-supervised learning
    • Erman J, Mahanti A, Arlitt M, Cohen I, Williamson C (2007) Offline/realtime traffic classification using semi-supervised learning. Perform Eval 64: 1194-1213.
    • (2007) Perform Eval , vol.64 , pp. 1194-1213
    • Erman, J.1    Mahanti, A.2    Arlitt, M.3    Cohen, I.4    Williamson, C.5
  • 14
    • 0034499376 scopus 로고    scopus 로고
    • A note on the utility of incremental learning
    • Giraud-Carrier C (2000) A note on the utility of incremental learning. AI Commun 13(4): 215-223.
    • (2000) AI Commun , vol.13 , Issue.4 , pp. 215-223
    • Giraud-Carrier, C.1
  • 16
    • 0003704318 scopus 로고    scopus 로고
    • University of California, Irvine, School of Information and Computer Sciences
    • Hettich S, Bay S (1999) The UCI KDD archive. University of California, Irvine, School of Information and Computer Sciences. http://kdd. ics. uci. edu/.
    • (1999) The UCI KDD archive
    • Hettich, S.1    Bay, S.2
  • 19
    • 37749050180 scopus 로고    scopus 로고
    • Dynamic weighted majority: an ensemble method for drifting concepts
    • Kolter JZ, Maloof MA (2007) Dynamic weighted majority: an ensemble method for drifting concepts. J Mach Learn Res 8: 2755-2790.
    • (2007) J Mach Learn Res , vol.8 , pp. 2755-2790
    • Kolter, J.Z.1    Maloof, M.A.2
  • 20
    • 84858710401 scopus 로고    scopus 로고
    • Mining recurring concept drift with limited labeled streaming data
    • Li P, Wu X, Hu X (2010) Mining recurring concept drift with limited labeled streaming data. In: JLMR: workshop and conference proceedings, vol 13, pp 241-252.
    • (2010) JLMR: Workshop and Conference Proceedings , vol.13 , pp. 241-252
    • Li, P.1    Wu, X.2    Hu, X.3
  • 21
    • 84885887319 scopus 로고    scopus 로고
    • Classification based on the optimal k-associated network
    • Lecture notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (LNICST), Berlin: Springer
    • Lopes AA, Bertini JR Jr, Motta R, Zhao L (2009) Classification based on the optimal k-associated network. In: Proceedings of the international conference on complex sciences: theory and applications (COMPLEX'09). Lecture notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (LNICST), vol 4. Springer, Berlin, pp 1167-1177.
    • (2009) Proceedings of the International Conference on Complex Sciences: Theory and Applications (Complex'09) , vol.4 , pp. 1167-1177
    • Lopes, A.A.1    Bertini Jr., J.R.2    Motta, R.3    Zhao, L.4
  • 23
    • 77949913486 scopus 로고    scopus 로고
    • The impact of diversity on online ensemble learning in the presence of concept drift
    • Minku L, White A, Yao X (2010) The impact of diversity on online ensemble learning in the presence of concept drift. IEEE Trans Knowl Data Eng 22: 730-742.
    • (2010) IEEE Trans Knowl Data Eng , vol.22 , pp. 730-742
    • Minku, L.1    White, A.2    Yao, X.3
  • 25
    • 54749119730 scopus 로고    scopus 로고
    • Particle competition for complex network community detection
    • 033107
    • Quiles M, Zhao L, Alonso RL, Romero RAF (2008) Particle competition for complex network community detection. Chaos 18: 033107.
    • (2008) Chaos , vol.18
    • Quiles, M.1    Zhao, L.2    Alonso, R.L.3    Romero, R.A.F.4
  • 27
    • 35248893285 scopus 로고    scopus 로고
    • Graph clustering
    • Schaeffer S (2007) Graph clustering. Comput Sci Rev 1: 27-34.
    • (2007) Comput Sci Rev , vol.1 , pp. 27-34
    • Schaeffer, S.1
  • 30
    • 58349089711 scopus 로고    scopus 로고
    • Adaptive active appearance model with incremental learning
    • Sung J, Kim D (2009) Adaptive active appearance model with incremental learning. Pattern Recognit Lett 30: 359-367.
    • (2009) Pattern Recognit Lett , vol.30 , pp. 359-367
    • Sung, J.1    Kim, D.2
  • 32
    • 34548583274 scopus 로고    scopus 로고
    • A tutorial on spectral clustering
    • von Luxburg U (2007) A tutorial on spectral clustering. Stat Comput 17: 395-416.
    • (2007) Stat Comput , vol.17 , pp. 395-416
    • von Luxburg, U.1
  • 34
    • 0030126609 scopus 로고    scopus 로고
    • Learning in the presence of concept drift and hidden contexts
    • Widmer G, Kubat M (1996) Learning in the presence of concept drift and hidden contexts. Mach Learn 23(1): 69-101.
    • (1996) Mach Learn , vol.23 , Issue.1 , pp. 69-101
    • Widmer, G.1    Kubat, M.2
  • 35
    • 42749097891 scopus 로고    scopus 로고
    • Non-stationary data sequence classification using online class priors estimation
    • Yang C, Zhou J (2008) Non-stationary data sequence classification using online class priors estimation. Pattern Recognit 41: 2656-2664.
    • (2008) Pattern Recognit , vol.41 , pp. 2656-2664
    • Yang, C.1    Zhou, J.2
  • 38
    • 33745456231 scopus 로고    scopus 로고
    • Tech Rep 1530, Computer-Science, University of Wisconsin-Madison
    • Zhu X (2008) Semi-supervised learning literature survey. Tech Rep 1530, Computer-Science, University of Wisconsin-Madison.
    • (2008) Semi-supervised learning literature survey
    • Zhu, X.1
  • 39
    • 33744955193 scopus 로고    scopus 로고
    • Semi-supervised learning with graphs
    • School of Computer Science, Carnegie Mellon University
    • Zhu X (2005) Semi-supervised learning with graphs. Tech Rep Doctoral Thesis, School of Computer Science, Carnegie Mellon University.
    • (2005) Tech Rep Doctoral Thesis
    • Zhu, X.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.