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Volumn 4, Issue 9, 2005, Pages 1033-1044

Model- and data-driven harmonic topographic maps

Author keywords

Smooth manifold identification; Tight clustering; Topographic maps

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; DATA STRUCTURES; LEARNING SYSTEMS; MATHEMATICAL MODELS; OPTIMIZATION;

EID: 24344454764     PISSN: 11092750     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (9)

References (14)
  • 3
    • 0000171332 scopus 로고    scopus 로고
    • Limitations of self-organizing maps for vector quantization and multidimensional scaling
    • M. C. Mozer, M. I. Jordan, and T. Petsche, editors, MIT Press, London, UK
    • A. Flexer. Limitations of self-organizing maps for vector quantization and multidimensional scaling. In M. C. Mozer, M. I. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9. Proceedings of the 1996 Conference, pages 445-51. MIT Press, London, UK, 1997.
    • (1997) Advances in Neural Information Processing Systems 9. Proceedings of the 1996 Conference , pp. 445-451
    • Flexer, A.1
  • 6
    • 0040111788 scopus 로고    scopus 로고
    • S-map: A network with a simple self-organization algorithm for generative topographic mappings
    • Michael I. Jordan, Michael J. Kearns, and Sara A. Solla, editors, The MIT Press
    • K. Kiviluoto and E. Oja. S-map: A network with a simple self-organization algorithm for generative topographic mappings. In Michael I. Jordan, Michael J. Kearns, and Sara A. Solla, editors, Advances in Neural Information Processing Systems, volume 10. The MIT Press, 1998.
    • (1998) Advances in Neural Information Processing Systems , vol.10
    • Kiviluoto, K.1    Oja, E.2
  • 8
    • 84958967653 scopus 로고    scopus 로고
    • Combining the self-organizing map and k-means clustering for on-line classification of sensor data
    • Kristof Van Laerhoven. Combining the self-organizing map and k-means clustering for on-line classification of sensor data. In ICANN, pages 464-469, 2001.
    • (2001) ICANN , pp. 464-469
    • Van Laerhoven, K.1
  • 9
    • 24344440094 scopus 로고    scopus 로고
    • Limitations of the som and the gtm
    • E. Pampalk. Limitations of the som and the gtm.
    • Pampalk, E.1
  • 13
    • 0012981871 scopus 로고    scopus 로고
    • Generalized k-harmonic means - Boosting in unsupervised learning
    • Technical report, HP Laboratories, Palo Alto, October
    • B. Zhang. Generalized k-harmonic means - boosting in unsupervised learning. Technical report, HP Laboratories, Palo Alto, October 2000.
    • (2000)
    • Zhang, B.1
  • 14
    • 0037530529 scopus 로고    scopus 로고
    • K-harmonic means - A data clustering algorithm
    • Technical report, HP Laboratories, Palo Alto, October
    • B. Zhang, M. Hsu, and U. Dayal. K-harmonic means - a data clustering algorithm. Technical report, HP Laboratories, Palo Alto, October 1999.
    • (1999)
    • Zhang, B.1    Hsu, M.2    Dayal, U.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.