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Volumn 5271 LNAI, Issue , 2008, Pages 220-228

Supervised classification fuzzy growing hierarchical SOM

Author keywords

[No Author keywords available]

Indexed keywords

FUZZY IF-THEN RULES; KOHONEN SELF-ORGANIZING MAPS; LEARNING METHODS; NEURO-FUZZY ALGORITHMS; PRIOR KNOWLEDGE; SUPERVISED CLASSIFICATION; TREE-LIKE STRUCTURES;

EID: 70350521484     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-87656-4_28     Document Type: Conference Paper
Times cited : (4)

References (12)
  • 1
    • 0026366218 scopus 로고
    • Neural-network-based fuzzy logic control and decision system
    • Lin, C.T., Lee, C.S.G.: Neural-network-based fuzzy logic control and decision system. IEEE Trans. Comput. 40, 1320-1336 (1991)
    • (1991) IEEE Trans. Comput , vol.40 , pp. 1320-1336
    • Lin, C.T.1    Lee, C.S.G.2
  • 2
    • 0027601884 scopus 로고
    • ANFIS: Adaptive-network based fuzzy inference systems
    • Jang, J.S.: ANFIS: Adaptive-network based fuzzy inference systems. IEEE Transactions on Systems 23(3), 665-685 (1993)
    • (1993) IEEE Transactions on Systems , vol.23 , Issue.3 , pp. 665-685
    • Jang, J.S.1
  • 3
    • 0026923465 scopus 로고
    • Learning and tuning fuzzy logic controllers through reinforcements
    • Berenji, H.R., Khedkar, P.: Learning and tuning fuzzy logic controllers through reinforcements. IEEE Trans. Neural Networks 3, 724-740 (1992)
    • (1992) IEEE Trans. Neural Networks , vol.3 , pp. 724-740
    • Berenji, H.R.1    Khedkar, P.2
  • 4
    • 0035670764 scopus 로고    scopus 로고
    • Evolving fuzzy neural networks for on-line supervised/unsupervised, knowledge-based learning
    • Kasabov, N.: Evolving fuzzy neural networks for on-line supervised/unsupervised, knowledge-based learning. IEEE Trans. Syst., Man, Cybern. 31(6), 902-918 (2001)
    • (2001) IEEE Trans. Syst., Man, Cybern , vol.31 , Issue.6 , pp. 902-918
    • Kasabov, N.1
  • 6
    • 38049172677 scopus 로고    scopus 로고
    • U*F Clustering: A new performance cluster-mining method based on segmentation of SOM
    • Paris, pp
    • Ultsch, A., Moutarde, F.: U*F Clustering: a new performance cluster-mining method based on segmentation of SOM. In: Proc. Workshop on Self-Organizing Maps, Paris, pp. 25-32 (2005)
    • (2005) Proc. Workshop on Self-Organizing Maps , pp. 25-32
    • Ultsch, A.1    Moutarde, F.2
  • 9
    • 33745814773 scopus 로고    scopus 로고
    • Granular self-organizing map (grSOM) for structure identification
    • Kaburlasos, V.G., Papadakis, S.E.: Granular self-organizing map (grSOM) for structure identification. Neural Networks 19(5), 623-643 (2006)
    • (2006) Neural Networks , vol.19 , Issue.5 , pp. 623-643
    • Kaburlasos, V.G.1    Papadakis, S.E.2
  • 10
    • 38449083583 scopus 로고
    • An Adaptive Rule Extraction with the Fuzzy Self-Organizing Map and a Comparison with Other Methods
    • September
    • Nomura, T., Miyoshi, M.: An Adaptive Rule Extraction with the Fuzzy Self-Organizing Map and a Comparison with Other Methods. In: Proc. ISUMA-NAFIPS 1995, pp. 311-316 (September 1995)
    • (1995) Proc. ISUMA-NAFIPS , pp. 311-316
    • Nomura, T.1    Miyoshi, M.2
  • 12
    • 0003389364 scopus 로고
    • Self Organizing Neural Networks for the Analysis and Representation of Data: Some Financial Cases
    • Martín, B., Serrano Cinca, C.: Self Organizing Neural Networks for the Analysis and Representation of Data: some Financial Cases. Neural Computing & Applications 1(2), 193-206 (1993)
    • (1993) Neural Computing & Applications , vol.1 , Issue.2 , pp. 193-206
    • Martín, B.1    Serrano Cinca, C.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.