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Volumn 17, Issue 11, 1996, Pages 1131-1139

Extended subspace methods of pattern recognition

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; CHARACTER SETS; DATA COMPRESSION; DECISION THEORY; LEARNING ALGORITHMS; MATHEMATICAL MODELS; PIECEWISE LINEAR TECHNIQUES; PROBABILITY DENSITY FUNCTION; STATISTICAL METHODS; VECTORS;

EID: 0030231285     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/0167-8655(96)00074-8     Document Type: Article
Times cited : (5)

References (16)
  • 1
    • 0025677015 scopus 로고
    • Rotation invariant image recognition using features selected via a systematic method
    • Alireza, K. and Y.H. Hong (1990). Rotation invariant image recognition using features selected via a systematic method. Pattern Recognition 23 (10), 1089-1101.
    • (1990) Pattern Recognition , vol.23 , Issue.10 , pp. 1089-1101
    • Alireza, K.1    Hong, Y.H.2
  • 2
    • 0028545911 scopus 로고
    • Constructive neural networks with piecewise interpolation capabilities for function approximation
    • Choi, C.H. and J. Y. Choi (1994). Constructive neural networks with piecewise interpolation capabilities for function approximation. IEEE Trans. Neural Networks 5, 936-943.
    • (1994) IEEE Trans. Neural Networks , vol.5 , pp. 936-943
    • Choi, C.H.1    Choi, J.Y.2
  • 3
    • 0019111672 scopus 로고
    • Clustering analysis
    • K.S. Fu, Ed., Springer, Berlin
    • Diday, E. and J.C. Simon (1980). Clustering analysis. In: K.S. Fu, Ed., Digital Pattern Recognition. Springer, Berlin, 47-94.
    • (1980) Digital Pattern Recognition , pp. 47-94
    • Diday, E.1    Simon, J.C.2
  • 5
    • 0004085318 scopus 로고
    • Growing cell structures - A self-organizing network for unsupervised and supervised learning
    • Internat. Computer Science Institute, Berkeley, CA
    • Fritzke, B. (1993). Growing cell structures - A self-organizing network for unsupervised and supervised learning. Technical Report TR-93-026, Internat. Computer Science Institute, Berkeley, CA.
    • (1993) Technical Report TR-93-026
    • Fritzke, B.1
  • 6
    • 0024053288 scopus 로고
    • Learned classification of sonar targets using a massively parallel network
    • Gorman, R.P. and J. Sejnowski (1988). Learned classification of sonar targets using a massively parallel network. IEEE Trans. Acoust. Speech Signal Process. 36 (7), 1135-1140.
    • (1988) IEEE Trans. Acoust. Speech Signal Process. , vol.36 , Issue.7 , pp. 1135-1140
    • Gorman, R.P.1    Sejnowski, J.2
  • 10
    • 0020880649 scopus 로고
    • The ALSM algorithm - An improved subspace method of classification
    • Oja, E. and M. Kuusela (1983). The ALSM algorithm - an improved subspace method of classification. Pattern Recognition 16 (4), 421-427.
    • (1983) Pattern Recognition , vol.16 , Issue.4 , pp. 421-427
    • Oja, E.1    Kuusela, M.2
  • 11
    • 0042870768 scopus 로고
    • Texture subspaces
    • P.A. Devijver and J. Kittler, Eds., Springer, Berlin
    • Oja, E. and J. Parkkinen (1986). Texture subspaces. In: P.A. Devijver and J. Kittler, Eds., Pattern Recognition - Theory and Applications. Springer, Berlin, 21-33.
    • (1986) Pattern Recognition - Theory and Applications , pp. 21-33
    • Oja, E.1    Parkkinen, J.2
  • 12
    • 0029359427 scopus 로고
    • A genetic approach for selection of (near-) optimal subsets of principal components for discrimination
    • Prakash, M. and M.N. Murty (1995a). A genetic approach for selection of (near-) optimal subsets of principal components for discrimination. Pattern Recognition Lett. 16, 781-787.
    • (1995) Pattern Recognition Lett. , vol.16 , pp. 781-787
    • Prakash, M.1    Murty, M.N.2
  • 13
    • 0041367977 scopus 로고
    • Hebbian learning subspace method: A new approach
    • communicated
    • Prakash, M. and M.N. Murty (1995b). Hebbian learning subspace method: A new approach. Pattern Recognition, communicated.
    • (1995) Pattern Recognition
    • Prakash, M.1    Murty, M.N.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.