대한전자공학회:학술대회논문집 (Proceedings of the IEEK Conference)
- 대한전자공학회 1998년도 하계종합학술대회논문집
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- Pages.933-935
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- 1998
HMM-Net 분류기의 효율적인 학습법
An efficient learning method of HMM-Net classifiers
초록
The HMM-Net is an architecture for a neural network that implements a hidden markov model (HMM). The architecture is developed for the purpose of combining the discriminant power of neural networks with the time-domain modeling capability of HMMs. Criteria used for learning HMM-Net classifiers are maximum likelihood(ML) and minimization of mean squared error(MMSE). In this paper we propose an efficient learning method of HMM_Net classifiers using a ML-MMSE hybrid criterion and report the results of an experimental study comparing the performance of HMM_Net classifiers trained by the gradient descent algorithm with the above criteria. Experimental results for the isolated numeric digits from /0/ to /9/ show that the performance of the proposed method is better than the others in the repects of learning and recognition rates.
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