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Volumn 131, Issue , 2014, Pages 15-22

Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size

Author keywords

Hyperspectral imaging; Learning Vector Quantization (LVQ); Radial Basis Function (RBF) networks; Raman spectroscopy; Supervised Neural Gas (SNG); Support Vector Machines (SVM)

Indexed keywords


EID: 84894067307     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2013.09.048     Document Type: Article
Times cited : (34)

References (17)
  • 1
    • 84255165109 scopus 로고    scopus 로고
    • Clustering of hyperspectral image signatures using neural gas
    • Seiffert U., Bollenbeck F. Clustering of hyperspectral image signatures using neural gas. Mach. Learn. Rep. 2010, 4:49-59.
    • (2010) Mach. Learn. Rep. , vol.4 , pp. 49-59
    • Seiffert, U.1    Bollenbeck, F.2
  • 3
    • 80755180926 scopus 로고    scopus 로고
    • Near infrared spectroscopic analysis of single malt scotch whisky on an optofluidic chip
    • Ashok P.C., Praveen B.B., Dholakia K. Near infrared spectroscopic analysis of single malt scotch whisky on an optofluidic chip. Opt. Exp. 2011, 19:22982-22992.
    • (2011) Opt. Exp. , vol.19 , pp. 22982-22992
    • Ashok, P.C.1    Praveen, B.B.2    Dholakia, K.3
  • 4
    • 0036791938 scopus 로고    scopus 로고
    • Generalized relevance learning vector quantization
    • Hammer B., Villmann T. Generalized relevance learning vector quantization. Neural Netw. 2002, 15:1059-1068.
    • (2002) Neural Netw. , vol.15 , pp. 1059-1068
    • Hammer, B.1    Villmann, T.2
  • 5
  • 6
    • 0000672424 scopus 로고
    • Fast learning in networks of locally tuned processing units
    • Moody J., Darken C.J. Fast learning in networks of locally tuned processing units. Neural Comput. 1989, 1:281-294.
    • (1989) Neural Comput. , vol.1 , pp. 281-294
    • Moody, J.1    Darken, C.J.2
  • 7
    • 11144273669 scopus 로고
    • The perceptron. a probabilistic model for information storage and organization in the brain
    • Rosenblatt F. The perceptron. a probabilistic model for information storage and organization in the brain. Psych. Rev. 1958, 65:386-408.
    • (1958) Psych. Rev. , vol.65 , pp. 386-408
    • Rosenblatt, F.1
  • 8
    • 0000646059 scopus 로고
    • Learning internal representations by error propagation
    • MIT Press, Cambridge, MA, USA, D.E. Rumelhart, J.L. McClelland (Eds.)
    • Rumelhart D., Hinton G., Williams R. Learning internal representations by error propagation. Parallel Distributed Processing 1986, 318-362. MIT Press, Cambridge, MA, USA. D.E. Rumelhart, J.L. McClelland (Eds.).
    • (1986) Parallel Distributed Processing , pp. 318-362
    • Rumelhart, D.1    Hinton, G.2    Williams, R.3
  • 12
    • 0000742931 scopus 로고
    • A neural-gas network learns topologies
    • North-Holland, Amsterdam, T. Kohonen, K. Mäkisara, O. Simula, J. Kangas (Eds.)
    • Martinetz T.M., Schulten K.J. A neural-gas network learns topologies. Artificial Neural Networks 1991, 397-402. North-Holland, Amsterdam. T. Kohonen, K. Mäkisara, O. Simula, J. Kangas (Eds.).
    • (1991) Artificial Neural Networks , pp. 397-402
    • Martinetz, T.M.1    Schulten, K.J.2
  • 13
    • 0027632248 scopus 로고
    • 'Neural-Gas' network for vector quantization and its application to time-series prediction
    • Martinetz T., Berkovich S., Schulten K. 'Neural-Gas' network for vector quantization and its application to time-series prediction. IEEE Trans. Neural Netw. 1993, 4:558-569.
    • (1993) IEEE Trans. Neural Netw. , vol.4 , pp. 558-569
    • Martinetz, T.1    Berkovich, S.2    Schulten, K.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.