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A prior-valued estimator applied to multinomial classification
Authors:MC Wang
Institution:Washington State University , Pullman, Washington
Abstract:A multinomial classification rule is proposed based on a prior-valued smoothing for the state probabilities. Asymptotically, the proposed rule has an error rate that converges uniformly and strongly to that of the Bayes rule. For a fixed sample size the prior-valued smoothing is effective in obtaining reason¬able classifications to the situations such as missing data. Empirically, the proposed rule is compared favorably with other commonly used multinomial classification rules via Monte Carlo sampling experiments
Keywords:multinomial classification  error rate  smoothes estimator  bahadur's model  monte carlo experiment
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