Pattern Classification Using Hybrid Monte Carlo Neural Networks 


Vol. 8,  No. 3, pp. 231-236, Jun.  2001
10.3745/KIPSTB.2001.8.3.231


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  Abstract

There are several algorithms for classification in modeling relations, patterns, and rules which exist in data. We learn to classify objects on the basis of instances presented to us, not by being given a set of classification rules. The hybrid monte carlo neural networks uses the probability distribution to express our knowledge about unknown parameters and update our knowledge by the law of probability as the evidence gathered from data. Also, the neural network models are designed for predicting an unknown category or quantity on the basis of known attributes by training. In this paper, we compare the misclassification error rates of hybrid monte carlo neural networks with those of other classification algorithms, CHAID, CART, and QUEST using several data sets. In terms of error rate, the Bayesian method works better than others in all data sets. The only trouble at this point is computing time.

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[IEEE Style]

S. H. Jun, S. Y. Choi, I. G. Oh, S. H. Lee, H. S. Jorn, "Pattern Classification Using Hybrid Monte Carlo Neural Networks," The KIPS Transactions:PartB , vol. 8, no. 3, pp. 231-236, 2001. DOI: 10.3745/KIPSTB.2001.8.3.231.

[ACM Style]

Sung Hae Jun, Seong Yong Choi, Im Geol Oh, Sang Ho Lee, and Hong Suk Jorn. 2001. Pattern Classification Using Hybrid Monte Carlo Neural Networks. The KIPS Transactions:PartB , 8, 3, (2001), 231-236. DOI: 10.3745/KIPSTB.2001.8.3.231.

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