Soft learning vector quantization

Neural Computation

Volumn 15, Issue 7, 2003, Pages 1589-1604

  Seo, Sambu ^a   Obermayer, Klaus ^a  

^a TECHNISCHE UNIVERSITÄT BERLIN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COST; LEARNING; NORMAL DISTRIBUTION; PHYSIOLOGY;

COSTS AND COST ANALYSIS; LEARNING; NORMAL DISTRIBUTION;

EID: 0038159964     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976603321891819     Document Type: Article

References (12)

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2. Duda, R. O., & Hart, P. E. (2000). Pattern classification and scene analysis. New York: Wiley.
3. Graepel, T., Burger, M., & Obermayer, K. (1997). Phase transduction in stochastic self-organizing maps. Physical Review E, 56, 3876-3890.
4. Kohonen, T. (1986). Learning vector quantization (Tech. Rep.). Otaniemi: Helsinki University of Technology.
5. Kohonen, T. (1990). Improved versions of learning vector quantization. In Neural Networks, IJCNN International Joint Conference on (Vol. 1, pp. 545-550). San Diego: IEE Computer Society Press.
6. Kohonen, T. (2001). Self organization maps. New York: Springer-Verlag.
7. Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J., & Torkkola, K. (1995). Lvq Pak: The learning vector quantization program package. Otaniemi: Helsinki University of Technology.
8. Komori, T., & Katagiri, S. (1992). Application of a generalized probabilistic descent method to dynamic time warping-based speech recognition. In Acoustics, Speech, and Signal Processing (pp. 497-500). San Francisco: IEEE Signal Processing Society Press.
9. McDermott, E., & Katagiri, S. (1994). Prototype-based minimum classification error/generalized probabilistic descent training for various speech units. Computer Speech and Language, 8(4), 351-368.
10. Miller, D., Rao, A. V., Rose, K., & Gersho, A. (1996). A global optimization technique for statistical classifier design. IEEE Transactions on Signal Processing, 44(12), 3108-3122.
11. Robbins, H., & Monro, S. (1951). A stochastic approximation method. Ann. Math. Stat., 22, 400-407.
12. Suykens, J. A. K., & Vandewalle, J. (1999). Multiclass least squares support vector machines. In IJCNN'99 International Joint Conference on Neural Networks. Washington, DC: IEEE Computer Society Press.