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Volumn , Issue , 2012, Pages 589-592

Cluster-dependent feature selection by multiple kernel self-organizing map

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATASETS; CLUSTERING APPROACH; INTRINSIC RELATION; LEARNING PROCEDURES; MULTIPLE KERNEL LEARNING; MULTIPLE KERNELS; OPTIMIZATION PROCEDURES;

EID: 84874569858     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (2)

References (19)
  • 4
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    • Spectral clustering and transductive learning with multiple views
    • D. Zhou and C. Burges. Spectral clustering and transductive learning with multiple views, Int. Conf. on Machine Learning, 2007.
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    • Zhou, D.1    Burges, C.2
  • 5
    • 78149489435 scopus 로고    scopus 로고
    • A novel multi-view agglomerative clustering algorithm based on ensemble of partitions on different views
    • H. Mirzaei. A novel multi-view agglomerative clustering algorithm based on ensemble of partitions on different views, Int. Conf. on Pattern Recognition, 2010.
    • (2010) Int. Conf. on Pattern Recognition
    • Mirzaei, H.1
  • 6
    • 78650072843 scopus 로고    scopus 로고
    • Multuple view clustering using a weighted combination of exemplar-based mixture models
    • G. F. Tzortzis and A. C. Likas. Multuple view clustering using a weighted combination of exemplar-based mixture models, IEEE Trans. Neural Networks, 2010.
    • (2010) IEEE Trans. Neural Networks
    • Tzortzis, G.F.1    Likas, A.C.2
  • 7
    • 0020068152 scopus 로고
    • Self-organized formation of topologically correct feature maps
    • T. Kohonen. Self-organized formation of topologically correct feature maps, Biological Cybernetics, 1982.
    • (1982) Biological Cybernetics
    • Kohonen, T.1
  • 8
    • 40849145323 scopus 로고    scopus 로고
    • Batch kernel SOM and related Laplacian methods for social network analysis
    • B. Romain, J. Bertrand, R. Fabrice, and V. Nathalie. Batch kernel SOM and related Laplacian methods for social network analysis, Neurocomputing, 2008.
    • (2008) Neurocomputing
    • Romain, B.1    Bertrand, J.2    Fabrice, R.3    Nathalie, V.4
  • 9
    • 33748422937 scopus 로고    scopus 로고
    • Kernel self-organising maps for classification
    • K. Lau, H. Yin, and S. Hubbard. Kernel self-organising maps for classification, Neurocomputing, 2006.
    • (2006) Neurocomputing
    • Lau, K.1    Yin, H.2    Hubbard, S.3
  • 14
  • 15
    • 36948999941 scopus 로고    scopus 로고
    • Irvine, CA: University of California, School of Information and Computer Science
    • A. Asuncion and D. J. Newman. UCI machine learning repository, Irvine, CA: University of California, School of Information and Computer Science, 2007.
    • (2007) UCI Machine Learning Repository
    • Asuncion, A.1    Newman, D.J.2
  • 16
    • 84932617705 scopus 로고    scopus 로고
    • Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
    • L. Fei-Fei, R. Fergus, P. Perona. Learning generative visual models from few training examples: an incremental bayesian approach tested on 101 object categories, Int. Conf. on Computer Vision and Pattern Recognition, 2004.
    • (2004) Int. Conf. on Computer Vision and Pattern Recognition
    • Fei-Fei, L.1    Fergus, R.2    Perona, P.3
  • 17
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    • Cluster ensembles-A knowledge reuse framework for combing multiple partitions
    • A. Strehl, J. Ghosh. Cluster ensembles-A knowledge reuse framework for combing multiple partitions, Journal of Machine Learning Research, 2002.
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    • Strehl, A.1    Ghosh, J.2
  • 19
    • 50649119439 scopus 로고    scopus 로고
    • Non-metric affinity propagation for unsupervised image categorization
    • D. Dueck and B. Frey. Non-metric affinity propagation for unsupervised image categorization, Int. Conf. on Computer Vision, 2007.
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    • Dueck, D.1    Frey, B.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.