The Optimum Classifier and the Performance Evaluation by Bayesian Approach
| Autor: | Fumitaka Kimura, Tetsushi Wakabayashi, Xuexian Han |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2000 |
| Předmět: |
education.field_of_study
Multivariate statistics Mean squared error Computer science Gaussian Population Bayesian probability Monte Carlo method Bayesian approach Sampling (statistics) Word error rate Conditional probability distribution Optimum classifier symbols.namesake Prior probability Statistics symbols Bayes error rate Statistical pattern recognition education Bayesian average Monte Carlo simulation |
| Zdroj: | Advances in Pattern Recognition ISBN: 9783540679462 SSPR/SPR |
| Popis: | This paper deals with the optimum classifier and the performance evaluation by the Bayesianapproach. Gaussian population with unknown parameters is assumed. The conditional density given a limited sample of the population has a relationship to the multivariate t-distribution. The mean error rate of the optimum classifier is theoretically evaluated by the quadrature of the conditional density. To verify the optimality of the classifier and the correctness of the mean error calculation, the results of Monte Carlo simulation employing a new sampling procedure are shown. It is also shown by the comparative study that the Bayesian formulas of the mean error rate have the following characteristics.1) The unknown population parameters are not required in its calculation.2) The expression is simple and clearly shows the limited sample effect on the mean error rate.3) The relationship between the prior parameters and the mean error rate is explicitly expressed. Berlin 901 Advances in pattern recognition : joint IAPR International Workshops SSPR 2000 and SPR 2000, Alicante, Spain, August 30-September 1, 2000 : proceedings Lecture notes in computer science |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|