Performance comparison of recognition systems: A Bayesian approach.

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  • 認識システムの性能比較 : ベイズ的接近法

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Abstract

This paper describes a Bayesian approach to performance comparison of recognition systems. Unlike a conventional statistical test, this method makes no decision whether there is a significant difference between the true recognition rate of System A and that of System B. Instead, it gives the probability of the event that the true recognition rate of A is higher than that of B given their recognition results. The probability is referred to as the superiority of A to B. This is similar to a numerical weather forecast, in which what is predicted is the probability of having a certain amount of rain, not a prospect of being sunny or rainy. The superiority is exemplified in various cases for the manner of inputting test data and observing the recognition results, and then its sensitivity for the difference between the respective sample recognition rates of A and B is investigated. All the results support that this method has natural properties which conform to our intuition. The relationship between the superiority in this method and the level of significance in statistical tests is also discussed.

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