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Volumn , Issue , 2005, Pages 387-395

Application of self organized maps and curvilinear component analysis to the discrimination of the vesuvius seismic signals

Author keywords

Seismic signals; Unsupervised clustering techniques

Indexed keywords

CURVILINEAR COMPONENT ANALYSIS; HIGH DIMENSIONAL DATA; PROJECTION METHOD; SEISMIC SIGNALS; SELF-ORGANIZED MAPS; UNSUPERVISED ANALYSIS; UNSUPERVISED CLUSTERING TECHNIQUE; UNSUPERVISED TECHNIQUES;

EID: 54949106704     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (7)

References (9)
  • 3
    • 0016495091 scopus 로고
    • Linear prediction: A tutorial review
    • J. Makhoul (1975), Linear Prediction: a Tutorial Review, Proceeding of IEEE, p. 561-580.
    • (1975) Proceeding of IEEE , pp. 561-580
    • Makhoul, J.1
  • 5
    • 0003410791 scopus 로고    scopus 로고
    • Series in Information Sciences Springer, Second edition
    • T. Kohonen (1997), Self-Organizing Maps, Series in Information Sciences, Vol. 30, Springer, Second edition.
    • (1997) Self-Organizing Maps , vol.30
    • Kohonen, T.1
  • 6
    • 0030736375 scopus 로고    scopus 로고
    • Curvilinear component analysis: A self-organizing neural network for nonlinear mapping of data sets
    • P. Demartines, J. Herault (1997), Curvilinear Component Analysis: A Self-Organizing Neural Network for Nonlinear Mapping of Data Sets, IEEE Transactions on Neural Networks, Vol. 8(1), p. 148-154.
    • (1997) IEEE Transactions on Neural Networks , vol.8 , Issue.1 , pp. 148-154
    • Demartines, P.1    Herault, J.2
  • 7
    • 1542680971 scopus 로고    scopus 로고
    • Nonlinear projection with curvilinear distances: Isomap versus curvilinear distance analysis
    • J.A. Lee, A Lendasse, M Verleysen (2004), Nonlinear Projection with Curvilinear Distances: Isomap versus Curvilinear Distance Analysis, Neurocomputing, Vol. 57, p. 49-76.
    • (2004) Neurocomputing , vol.57 , pp. 49-76
    • Lee, J.A.1    Lendasse, A.2    Verleysen, M.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.