Discriminative learning for minimum error classification [pattern recognition]

@article{Juang1992DiscriminativeLF,
  title={Discriminative learning for minimum error classification [pattern recognition]},
  author={Biing-Hwang Juang and Shigeru Katagiri},
  journal={IEEE Trans. Signal Processing},
  year={1992},
  volume={40},
  pages={3043-3054}
}
Recently, due to the advent of artificial neural networks and learning vector quantizers, there is a resurgent interest in reexamining the classical techniques of discriminant analysis to suit the new classifier structures. One of the particular problems of interest is minimum error classification in which the misclassification probability is to be minimized based on a given set of training samples. In this paper, we propose a new formulation for the minimum error classification problem… CONTINUE READING
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References

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New discriminative training algorithm based on the generalized probabilistic descent method,

  • S. Katagiri, C. H. Lee, B. H. Juang
  • IEEE Workshop Neural Networks for Signal…
  • 1991

Systematic explanation of learning vector quantization and multilayer perceptron-proposition of distance network,

  • S. Katagiri
  • IEICE, MBE 88-72,
  • 1988

S’79-M’8O-SM’87-F’92) received the B.Sc. degree in electrical engineering from National Taiwan University, Taipei, in 1973 and the M.Sc

  • Biing-Hwang Juang
  • 1988

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